CHANNEL_NAME stringclasses 1
value | URL stringlengths 43 43 | TITLE stringlengths 61 100 | DESCRIPTION stringclasses 6
values | TRANSCRIPTION stringlengths 2.07k 14.5k | SEGMENTS stringlengths 3.72k 25k |
|---|---|---|---|---|---|
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=y8JgiWcUnU8 | 1.1 Machine Learning Overview | Welcome to machine learning!--[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome to machine learning. What is machine learning? You probably use it many times a day without even knowing it. Anytime you want to find out something like, how do I make a sushi roll, you can do a web search on Google, Bing, or Baidu to find out. And that works so well because their machine learning software has... | [{"start": 0.0, "end": 6.04, "text": " Welcome to machine learning."}, {"start": 6.04, "end": 8.16, "text": " What is machine learning?"}, {"start": 8.16, "end": 12.280000000000001, "text": " You probably use it many times a day without even knowing it."}, {"start": 12.280000000000001, "end": 17.16, "text": " Anytime y... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=AISftYVyS50 | 1.2 Machine Learning Overview | What is machine learning? --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, what is machine learning? In this video you learn the definition of what it is, and also get a sense of when you might want to apply it. Let's take a look together. Here's the definition of what is machine learning that is attributed to Arthur Samuel. He defined machine learning as the feeble study that gives comp... | [{"start": 0.0, "end": 3.0, "text": " So, what is machine learning?"}, {"start": 3.0, "end": 8.08, "text": " In this video you learn the definition of what it is, and also get a sense of when you might"}, {"start": 8.08, "end": 9.08, "text": " want to apply it."}, {"start": 9.08, "end": 10.68, "text": " Let's take a lo... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=hHYcNPfbBXQ | 1.3 Machine Learning Overview | Applications of machine learning -- [Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself. You learn about the most important machine learning algorithms, some of which are exactly what's being used in large AI or large tech companies today, and you get a sense of what is the state of the... | [{"start": 0.0, "end": 6.88, "text": " In this class, you learn about the state of the art and also practice implementing machine"}, {"start": 6.88, "end": 9.4, "text": " learning algorithms yourself."}, {"start": 9.4, "end": 13.88, "text": " You learn about the most important machine learning algorithms, some of which... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=EZN_uM3J3kI | 1.4 Machine Learning Overview | Supervised learning part 1 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Machine learning is creating tremendous economic value today. I think 99% of the economic value created by machine learning today is through one type of machine learning, which is called supervised learning. Let's take a look at what that means. Supervised machine learning, or more commonly supervised learning, refers... | [{"start": 0.0, "end": 5.84, "text": " Machine learning is creating tremendous economic value today."}, {"start": 5.84, "end": 11.08, "text": " I think 99% of the economic value created by machine learning today is through one type"}, {"start": 11.08, "end": 13.92, "text": " of machine learning, which is called supervi... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=l16C3PKiHKg | 1.5 Machine Learning Overview | Supervised learning part 2 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, supervised learning algorithms learn to predict input-output or x-to-y mappings, and in the last video you saw that regression algorithms, which is a type of supervised learning algorithm, learn to predict numbers out of infinitely many possible numbers. There's a second major type of supervised learning algorithm... | [{"start": 0.0, "end": 9.48, "text": " So, supervised learning algorithms learn to predict input-output or x-to-y mappings, and"}, {"start": 9.48, "end": 14.280000000000001, "text": " in the last video you saw that regression algorithms, which is a type of supervised"}, {"start": 14.280000000000001, "end": 19.64, "text... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=yzAFnfHYH9E | 1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | After supervised learning, the most widely used form of machine learning is unsupervised learning. Let's take a look at what that means. We've talked about supervised learning, and this video is about unsupervised learning. But don't let the name unsupervised fool you. Unsupervised learning is, I think, just as super ... | [{"start": 0.0, "end": 7.5200000000000005, "text": " After supervised learning, the most widely used form of machine learning is unsupervised"}, {"start": 7.5200000000000005, "end": 8.52, "text": " learning."}, {"start": 8.52, "end": 11.36, "text": " Let's take a look at what that means."}, {"start": 11.36, "end": 16.8... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=u7Y_b04upmQ | 1.7 Machine Learning Overview | Unsupervised learning part 2 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw what is unsupervised learning and one type of unsupervised learning called clustering. Let's give a slightly more formal definition of unsupervised learning and take a quick look at some other types of unsupervised learning other than clustering. Whereas in supervised learning, the data come... | [{"start": 0.0, "end": 6.88, "text": " In the last video, you saw what is unsupervised learning and one type of unsupervised learning"}, {"start": 6.88, "end": 7.88, "text": " called clustering."}, {"start": 7.88, "end": 13.52, "text": " Let's give a slightly more formal definition of unsupervised learning and take a q... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=SRDUSIcnW8M | 1.8 Machine Learning Overview | Jupyter Notebooks --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So far in the videos, you've seen supervised learning and unsupervised learning, and also examples of both. For you to more deeply understand these concepts, I'd like to invite you in this class to see, run, and maybe later write code yourself to implement these concepts. The most widely used tool by machine learning ... | [{"start": 0.0, "end": 8.6, "text": " So far in the videos, you've seen supervised learning and unsupervised learning, and also"}, {"start": 8.6, "end": 10.6, "text": " examples of both."}, {"start": 10.6, "end": 16.72, "text": " For you to more deeply understand these concepts, I'd like to invite you in this class to ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=isx7QB_j4jY | 1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, we'll look at what the overall process of supervised learning is like. Specifically, you see the first model of this course, a linear regression model. That just means filling a straight line to your data. It's probably the most widely used learning algorithm in the world today. And as you get familiar ... | [{"start": 0.0, "end": 8.0, "text": " In this video, we'll look at what the overall process of supervised learning is like."}, {"start": 8.0, "end": 14.24, "text": " Specifically, you see the first model of this course, a linear regression model."}, {"start": 14.24, "end": 17.52, "text": " That just means filling a str... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=vrTHO5zRq6s | 1.10 Machine Learning Overview | Linear regression model part 2 --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's look in this video at the process of how supervised learning works. Supervised learning algorithm will input a data set and then what exactly does it do and what does it output? Let's find out in this video. Recall that a training set in supervised learning includes both the input features, such as the size of t... | [{"start": 0.0, "end": 7.38, "text": " Let's look in this video at the process of how supervised learning works."}, {"start": 7.38, "end": 11.3, "text": " Supervised learning algorithm will input a data set and then what exactly does it do"}, {"start": 11.3, "end": 12.84, "text": " and what does it output?"}, {"start":... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ZzeDtSmrRoU | 1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In order to implement linear regression, the first key step is for us to define something called a cost function. This is something we'll build in this video. And the cost function will tell us how well the model is doing so that we can try to get it to do better. Let's look at what this means. Recall that you have a ... | [{"start": 0.0, "end": 6.32, "text": " In order to implement linear regression, the first key step is for us to define something"}, {"start": 6.32, "end": 8.32, "text": " called a cost function."}, {"start": 8.32, "end": 10.8, "text": " This is something we'll build in this video."}, {"start": 10.8, "end": 16.04, "text... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=33VvmIZof0E | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | We've seen the mathematical definition of the cost function. Now let's build some intuition about what the cost function is really doing. In this video, we'll walk through one example to see how the cost function can be used to find the best parameters for your model. I know this video is a little bit longer than the ... | [{"start": 0.0, "end": 5.76, "text": " We've seen the mathematical definition of the cost function."}, {"start": 5.76, "end": 10.28, "text": " Now let's build some intuition about what the cost function is really doing."}, {"start": 10.28, "end": 14.88, "text": " In this video, we'll walk through one example to see how... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=75eDNh4A07Q | 1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw one visualization of the cost function J of w or J of wb. Let's look at some further, richer visualizations so you can get an even better intuition about what the cost function is doing. Here is what we've seen so far. There's the model, the model's parameters w and b, the cost function J of... | [{"start": 0.0, "end": 8.700000000000001, "text": " In the last video, you saw one visualization of the cost function J of w or J of wb."}, {"start": 8.700000000000001, "end": 14.18, "text": " Let's look at some further, richer visualizations so you can get an even better intuition about"}, {"start": 14.18, "end": 16.7... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=GY0KyF3h8hA | 1.14 Machine Learning Overview | Visualizing examples --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's look at some more visualizations of w and b. Here's one example. Over here, you have a particular point on the graph J. For this point, w equals about negative 0.15 and b equals about 800. So this point corresponds to one pair of values for w and b that use a particular cost J. And in fact, this particular pair ... | [{"start": 0.0, "end": 7.6000000000000005, "text": " Let's look at some more visualizations of w and b."}, {"start": 7.6000000000000005, "end": 9.8, "text": " Here's one example."}, {"start": 9.8, "end": 17.32, "text": " Over here, you have a particular point on the graph J. For this point, w equals about"}, {"start": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=6W3tzcOnWfQ | 1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome back. In the last video we saw visualizations of the cost function j and how you can try different choices of the parameters w and b and see what cost value that gets you. It would be nice if we had a more systematic way to find the values of w and b that result in the smallest possible cost j of w, b. It turn... | [{"start": 0.0, "end": 8.2, "text": " Welcome back. In the last video we saw visualizations of the cost function j and how you can try"}, {"start": 8.2, "end": 14.32, "text": " different choices of the parameters w and b and see what cost value that gets you. It"}, {"start": 14.32, "end": 20.080000000000002, "text": " ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Dz4JZzgn-hg | 1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's take a look at how you can actually implement the gradient descent algorithm. Let me write down the gradient descent algorithm. Here it is. On each step, w, the parameter, is updated to the old value of w minus alpha times this term d over dw of the cost function j of wb. So what this expression is saying is upd... | [{"start": 0.0, "end": 10.040000000000001, "text": " Let's take a look at how you can actually implement the gradient descent algorithm."}, {"start": 10.040000000000001, "end": 14.280000000000001, "text": " Let me write down the gradient descent algorithm."}, {"start": 14.280000000000001, "end": 15.4, "text": " Here it... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=rGvoO8U2Ozc | 1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Now, let's dive more deeply into gradient descent to gain better intuition about what it's doing and why it might make sense. Here's the gradient descent algorithm that you saw in the previous video. And as a reminder, this variable, this Greek symbol alpha is the learning rate. And the learning rate controls how big ... | [{"start": 0.0, "end": 7.88, "text": " Now, let's dive more deeply into gradient descent to gain better intuition about what"}, {"start": 7.88, "end": 10.84, "text": " it's doing and why it might make sense."}, {"start": 10.84, "end": 15.52, "text": " Here's the gradient descent algorithm that you saw in the previous v... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Eu8lt4j9xiU | 1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The choice of the learning rate alpha will have a huge impact on the efficiency of your implementation of gradient descent, and if alpha, the learning rate, is chosen poorly, gradient descent may not even work at all. In this video, let's take a deeper look at the learning rate. This will also help you choose better l... | [{"start": 0.0, "end": 7.12, "text": " The choice of the learning rate alpha will have a huge impact on the efficiency of your"}, {"start": 7.12, "end": 13.280000000000001, "text": " implementation of gradient descent, and if alpha, the learning rate, is chosen poorly,"}, {"start": 13.280000000000001, "end": 15.94, "te... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ZSg_NglG3aA | 1.19 Machine Learning Overview| Gradient descent for Linear Regression--[Machine Learning|Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, previously you took a look at the linear regression model and then the cost function and then the gradient descent algorithm. In this video, we're going to put it all together and use the square error cost function for the linear regression model with gradient descent. This will allow us to train the linear regres... | [{"start": 0.0, "end": 7.5200000000000005, "text": " So, previously you took a look at the linear regression model and then the cost function"}, {"start": 7.5200000000000005, "end": 10.68, "text": " and then the gradient descent algorithm."}, {"start": 10.68, "end": 15.8, "text": " In this video, we're going to put it ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=3xyYI4wPuTs | 1.20 Machine Learning Overview | Running gradient descent --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's see what happens when you run gradient descent for linear regression. Let's go see the algorithm in action. Here's a plot of the model and data on the upper left, and a contour plot of the cost function on the upper right. And at the bottom is the surface plot of the same cost function. Often W and B will both b... | [{"start": 0.0, "end": 6.4, "text": " Let's see what happens when you run gradient descent for linear regression."}, {"start": 6.4, "end": 8.88, "text": " Let's go see the algorithm in action."}, {"start": 8.88, "end": 15.56, "text": " Here's a plot of the model and data on the upper left, and a contour plot of the cos... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=fX86EFWljY0 | 2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome back. In this week, we'll learn to make linear regression much faster and much more powerful, and by the end of this week, you'll be two-thirds of the way to finishing this first course. Let's start by looking at a version of linear regression that can look at not just one feature, but a lot of different featu... | [{"start": 0.0, "end": 7.2, "text": " Welcome back. In this week, we'll learn to make linear regression much faster and much"}, {"start": 7.2, "end": 12.620000000000001, "text": " more powerful, and by the end of this week, you'll be two-thirds of the way to finishing"}, {"start": 12.620000000000001, "end": 17.42, "tex... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=G8yfD_Xu7Ko | 2.2 Linear Regression with Multiple Variables | Vectorization part 1 --[Machine Learning |Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, you see a very useful idea called vectorization. When you're implementing a learning algorithm, using vectorization will both make your code shorter and also make it run much more efficiently. Learning how to write vectorized code will allow you to also take advantage of modern numerical linear algebra ... | [{"start": 0.0, "end": 7.28, "text": " In this video, you see a very useful idea called vectorization."}, {"start": 7.28, "end": 11.96, "text": " When you're implementing a learning algorithm, using vectorization will both make your code"}, {"start": 11.96, "end": 16.740000000000002, "text": " shorter and also make it ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=nRGG50GDNAA | 2.3 Linear Regression with Multiple Variables | Vectorization part 2 --[Machine Learning |Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | I remember when I first learned about vectorization, I spent many hours on my computer taking an un-vectorized version of an algorithm, running it, see how long it ran, and then running a vectorized version of the code and seeing how much faster that ran. And I just spent hours playing with that. And it frankly blew m... | [{"start": 0.0, "end": 8.08, "text": " I remember when I first learned about vectorization, I spent many hours on my computer taking an"}, {"start": 8.08, "end": 12.9, "text": " un-vectorized version of an algorithm, running it, see how long it ran, and then running"}, {"start": 12.9, "end": 16.2, "text": " a vectorize... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=odAhNw-e4o0 | 2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, you've learned about gradient descent, about multiple linear regression, and also vectorization. Let's put it all together to implement gradient descent for multiple linear regression with vectorization. This would be cool. Let's quickly review what multiple linear regression looks like. Using our previous notatio... | [{"start": 0.0, "end": 7.2, "text": " So, you've learned about gradient descent, about multiple linear regression, and also"}, {"start": 7.2, "end": 8.2, "text": " vectorization."}, {"start": 8.2, "end": 13.16, "text": " Let's put it all together to implement gradient descent for multiple linear regression with"}, {"st... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=AbtWSXHPfS0 | 2.5 Practical Tips for Linear Regression | Feature scaling part 1-- [Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, welcome back. Let's take a look at some techniques that will make gradient descent work much better. In this video, you see a technique called feature scaling that will enable gradient descent to run much faster. Let's start by taking a look at the relationship between the size of a feature, that is, how big are t... | [{"start": 0.0, "end": 4.28, "text": " So, welcome back."}, {"start": 4.28, "end": 8.8, "text": " Let's take a look at some techniques that will make gradient descent work much better."}, {"start": 8.8, "end": 13.36, "text": " In this video, you see a technique called feature scaling that will enable gradient"}, {"star... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=g9bkFTnM-7k | 2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's look at how you can implement feature scaling to take features that take on very different ranges of values and scale them to have comparable ranges of value to each other. So how do you actually scale features? Well, if X1 ranges from 3 to 2000, one way to get a scale version of X1 is to take each original X1 v... | [{"start": 0.0, "end": 6.6000000000000005, "text": " Let's look at how you can implement feature scaling to take features that take on very"}, {"start": 6.6000000000000005, "end": 11.28, "text": " different ranges of values and scale them to have comparable ranges of value to each"}, {"start": 11.28, "end": 12.28000000... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=9w3HRwTRVUE | 2.7 Practical Tips for Linear Regression | Checking gradient descent for convergence -- ML Andrew Ng | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | When running gradient descent, how can you tell if it is converging? That is, whether it's helping you to find parameters close to the global minimum of the cost function. By learning to recognize what a well-running implementation of gradient descent looks like, we will also, in a later video, be better able to choos... | [{"start": 0.0, "end": 5.96, "text": " When running gradient descent, how can you tell if it is converging?"}, {"start": 5.96, "end": 10.32, "text": " That is, whether it's helping you to find parameters close to the global minimum of"}, {"start": 10.32, "end": 12.16, "text": " the cost function."}, {"start": 12.16, "e... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=j4uDxRNjYlA | 2.8 Practical Tips for Linear Regression | | Choosing the learning rate-[MachineLearning|Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Your learning algorithm will run much better with an appropriate choice of learning rate. If it's too small, it will run very slowly, and if it's too large, it may not even converge. Let's take a look at how you can choose a good learning rate for your model. Concretely, if you plot the cost for a number of iterations... | [{"start": 0.0, "end": 6.76, "text": " Your learning algorithm will run much better with an appropriate choice of learning rate."}, {"start": 6.76, "end": 12.64, "text": " If it's too small, it will run very slowly, and if it's too large, it may not even converge."}, {"start": 12.64, "end": 16.72, "text": " Let's take ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=sO_JcsxQRz8 | 2.9 Practical Tips for Linear Regression | Feature engineering --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The choice of features can have a huge impact on your learning algorithm's performance. In fact, for many practical applications, choosing or entering the right features is a critical step to making the algorithm work well. In this video, let's take a look at how you can choose or engineer the most appropriate feature... | [{"start": 0.0, "end": 6.0, "text": " The choice of features can have a huge impact on your learning algorithm's performance."}, {"start": 6.0, "end": 11.120000000000001, "text": " In fact, for many practical applications, choosing or entering the right features is"}, {"start": 11.120000000000001, "end": 14.6, "text": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=UobsU2oQwBA | 2.10 Practical Tips for Linear Regression | Polynomial regression --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So far, we've just been fitting straight lines to our data. Let's take the ideas of multiple linear regression and feature engineering to come up with a new algorithm called polynomial regression, which will let you fit curves, nonlinear functions, to your data. Let's say you have a housing data set that looks like th... | [{"start": 0.0, "end": 7.54, "text": " So far, we've just been fitting straight lines to our data."}, {"start": 7.54, "end": 12.56, "text": " Let's take the ideas of multiple linear regression and feature engineering to come up with a"}, {"start": 12.56, "end": 18.04, "text": " new algorithm called polynomial regressio... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=wul3SmrpAeY | 3.1 Classification | Motivations --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome to the third week of this course. By the end of this week, you have completed the first course of this specialization. So let's jump in. Last week, you learned about linear regression, which predicts a number. This week, you learned about classification, where your output variable y can take on only one of a s... | [{"start": 0.0, "end": 3.72, "text": " Welcome to the third week of this course."}, {"start": 3.72, "end": 8.4, "text": " By the end of this week, you have completed the first course of this specialization."}, {"start": 8.4, "end": 10.200000000000001, "text": " So let's jump in."}, {"start": 10.200000000000001, "end": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Pm8mRCZmYiU | 3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's talk about logistic regression, which is probably the single most widely used classification algorithm in the world. This is something that I use all the time in my work. Let's continue with the example of classifying whether a tumor is malignant, where as before we're going to use the label 1 or yes the positiv... | [{"start": 0.0, "end": 6.0600000000000005, "text": " Let's talk about logistic regression, which is probably the single most widely used classification"}, {"start": 6.0600000000000005, "end": 7.54, "text": " algorithm in the world."}, {"start": 7.54, "end": 10.94, "text": " This is something that I use all the time in ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=QJdIpRcL_4U | 3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you learned about the logistic regression model. Now, let's take a look at the decision boundary to get a better sense of how logistic regression is computing its predictions. To recap, here's how the logistic regression model's outputs are computed in two steps. In the first step, you compute z as ... | [{"start": 0.0, "end": 5.2, "text": " In the last video, you learned about the logistic regression model."}, {"start": 5.2, "end": 11.72, "text": " Now, let's take a look at the decision boundary to get a better sense of how logistic regression"}, {"start": 11.72, "end": 13.92, "text": " is computing its predictions."}... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=E-ZcnndnY2I | 3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Remember that the cost function gives you a way to measure how well a specific set of parameters fits the training data, and it thereby gives you a way to try to choose better parameters. In this video, we'll look at how the squared error cost function is not an ideal cost function for logistic regression, and we'll t... | [{"start": 0.0, "end": 6.96, "text": " Remember that the cost function gives you a way to measure how well a specific set of"}, {"start": 6.96, "end": 12.92, "text": " parameters fits the training data, and it thereby gives you a way to try to choose better"}, {"start": 12.92, "end": 13.92, "text": " parameters."}, {"s... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=wB08Jlmhi24 | 3.5 Cost Function | Simplified Cost Function for Logistic Regression --[Machine Learning|Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw the loss function and the cost function for logistic regression. In this video, you'll see a slightly simpler way to write out the loss and cost functions so that the implementation can be a bit simpler when we get to gradient descent for fitting the parameters of a logistic regression model... | [{"start": 0.0, "end": 8.16, "text": " In the last video, you saw the loss function and the cost function for logistic regression."}, {"start": 8.16, "end": 13.9, "text": " In this video, you'll see a slightly simpler way to write out the loss and cost functions"}, {"start": 13.9, "end": 19.0, "text": " so that the imp... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=D59jK8T9dfI | 3.6 Gradient Descent | Gradient Descent Implementation --[Machine Learning | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | To fit the parameters of a logistic regression model, we're going to try to find the values of the parameters w and b that minimise the cost function j of w and b. And we're going to apply gradient descent to do this. Let's take a look at how. In this video, we'll focus on how to find a good choice of the parameters w... | [{"start": 0.0, "end": 7.28, "text": " To fit the parameters of a logistic regression model, we're going to try to find the values"}, {"start": 7.28, "end": 12.92, "text": " of the parameters w and b that minimise the cost function j of w and b."}, {"start": 12.92, "end": 16.4, "text": " And we're going to apply gradie... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=wBx3NZ0ucgc | 3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Now you've seen a couple of different learning algorithms, linear regression and logistic regression. They work well for many tasks, but sometimes in an application the algorithm could run into a problem called overfitting, which can cause it to perform poorly. What I'd like to do in this video is to show you what is ... | [{"start": 0.0, "end": 7.28, "text": " Now you've seen a couple of different learning algorithms, linear regression and logistic"}, {"start": 7.28, "end": 8.28, "text": " regression."}, {"start": 8.28, "end": 13.88, "text": " They work well for many tasks, but sometimes in an application the algorithm could run"}, {"st... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ce4CPW8AFE4 | 3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Later in this specialization, we'll talk about debugging and diagnosing things that can go wrong with learning algorithms. You also learn about specific tools to recognize when overfitting and underfitting may be occurring. But for now, when you think overfitting has occurred, let's talk about what you can do to addre... | [{"start": 0.0, "end": 7.04, "text": " Later in this specialization, we'll talk about debugging and diagnosing things that"}, {"start": 7.04, "end": 9.52, "text": " can go wrong with learning algorithms."}, {"start": 9.52, "end": 15.8, "text": " You also learn about specific tools to recognize when overfitting and unde... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=SCj3h47dKL0 | 3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, we saw that regularization tries to make the parameter values w1 through wn small to reduce overfitting. In this video, we'll build on that intuition and develop a modified cost function for your learning algorithm they can use to actually apply regularization. Let's jump in. Recall this example fro... | [{"start": 0.0, "end": 7.28, "text": " In the last video, we saw that regularization tries to make the parameter values w1 through"}, {"start": 7.28, "end": 10.8, "text": " wn small to reduce overfitting."}, {"start": 10.8, "end": 15.84, "text": " In this video, we'll build on that intuition and develop a modified cost... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=yRSKygmsvSI | 3.10 Regularization to Reduce Overfitting | Regularized linear regression-- [ML | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, we'll figure out how to get gradient descent to work with regularized linear regression. Let's jump in. Here's the cost function we've come up with in the last video for regularized linear regression. The first part is the usual squared error cost function. And now you have this additional regularizatio... | [{"start": 0.0, "end": 6.140000000000001, "text": " In this video, we'll figure out how to get gradient descent to work with regularized"}, {"start": 6.140000000000001, "end": 7.140000000000001, "text": " linear regression."}, {"start": 7.140000000000001, "end": 8.14, "text": " Let's jump in."}, {"start": 8.14, "end": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=MFp4uQMQ1rk | 3.11 Regularization to Reduce Overfitting | Regularized logistic regression-- [ML | Andrew Ng] | First Course:
Supervised Machine Learning : Regression and Classification.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, you see how to implement regularized logistic regression. Just as the gradient update for logistic regression has seemed surprisingly similar to the gradient update for linear regression, you find that the gradient descent update for regularized logistic regression will also look similar to the update f... | [{"start": 0.0, "end": 6.4, "text": " In this video, you see how to implement regularized logistic regression."}, {"start": 6.4, "end": 11.8, "text": " Just as the gradient update for logistic regression has seemed surprisingly similar to the gradient"}, {"start": 11.8, "end": 16.44, "text": " update for linear regress... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=cuU8pCflXCo | 4.1 Advanced Learning Algorithms | Welcome! --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome to course two of this machine learning specialization. In this course, you learn about neural networks, also called deep learning algorithms, as well as decision trees. These are some of the most powerful and widely used machine learning algorithms, and you get to implement them and get them to work for yourse... | [{"start": 0.0, "end": 4.9, "text": " Welcome to course two of this machine learning specialization."}, {"start": 4.9, "end": 9.98, "text": " In this course, you learn about neural networks, also called deep learning algorithms, as well"}, {"start": 9.98, "end": 11.94, "text": " as decision trees."}, {"start": 11.94, "... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=QpJ35mMLIOA | 4.2 Neural Networks Intuition |Neurons and the brain --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | When neural networks were first invented many decades ago, the original motivation was the right software that could mimic how the human brain or how the biological brain learns and thinks. And even though today, neural networks, sometimes also called artificial neural networks, have become very different than how any... | [{"start": 0.0, "end": 5.64, "text": " When neural networks were first invented many decades ago, the original motivation was the"}, {"start": 5.64, "end": 11.64, "text": " right software that could mimic how the human brain or how the biological brain learns and"}, {"start": 11.64, "end": 12.64, "text": " thinks."}, {... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=cHFM92fhpew | 4.3 Neural Networks Intuition | Demand Prediction --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | To illustrate how neural networks work, let's start with an example. We'll use an example from demand prediction in which you look at a product and try to predict, will this product be a top seller or not? Let's take a look. In this example, you're selling t-shirts and you would like to know if a particular t-shirt wi... | [{"start": 0.0, "end": 5.44, "text": " To illustrate how neural networks work, let's start with an example."}, {"start": 5.44, "end": 10.0, "text": " We'll use an example from demand prediction in which you look at a product and try to"}, {"start": 10.0, "end": 13.44, "text": " predict, will this product be a top selle... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=3RIUt73mj3Q | 4.4 Neural Networks Intuition | Example Recognizing Images--[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw how a neural network works in a demand prediction example. Let's take a look at how you can apply a similar type of idea to a computer vision application. Let's dive in. If you're building a face recognition application, you might want to train, say, a neural network that takes this input to... | [{"start": 0.0, "end": 6.92, "text": " In the last video, you saw how a neural network works in a demand prediction example."}, {"start": 6.92, "end": 12.56, "text": " Let's take a look at how you can apply a similar type of idea to a computer vision application."}, {"start": 12.56, "end": 13.56, "text": " Let's dive i... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=YmDM4Bq-dyA | 4.5 Neural Networks Model | Neural Network Layer --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The fundamental building block of most modern neural networks is a layer of neurons. In this video, you learn how to construct a layer of neurons, and once you have that down, you'll be able to take those building blocks and put them together to form a large neural network. Let's take a look at how a layer of neurons ... | [{"start": 0.0, "end": 8.68, "text": " The fundamental building block of most modern neural networks is a layer of neurons."}, {"start": 8.68, "end": 13.280000000000001, "text": " In this video, you learn how to construct a layer of neurons, and once you have that"}, {"start": 13.280000000000001, "end": 17.92, "text": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=4-2FOgsMOpk | 4.6 Neural Networks Model | More complex neural networks --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you learned about the neural network layer and how that takes as input a vector of numbers and in turn outputs another vector of numbers. In this video, let's use that layer to build a more complex neural network. And through this, I hope that the notation that we're using for neural networks will b... | [{"start": 0.0, "end": 7.2, "text": " In the last video, you learned about the neural network layer and how that takes as input"}, {"start": 7.2, "end": 12.6, "text": " a vector of numbers and in turn outputs another vector of numbers."}, {"start": 12.6, "end": 17.6, "text": " In this video, let's use that layer to bui... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=L1fsfAbq-q8 | 4.7 Neural Networks Model |Inference : making predictions (forward propagation)- [ML- Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's take what we've learned and put it together into an algorithm to let your neural network make inferences or make predictions. This will be an algorithm called forward propagation. Let's take a look. I'm going to use as a multi-fingered example, handwritten digit recognition. And for simplicity, we're just going ... | [{"start": 0.0, "end": 6.48, "text": " Let's take what we've learned and put it together into an algorithm to let your neural network"}, {"start": 6.48, "end": 9.48, "text": " make inferences or make predictions."}, {"start": 9.48, "end": 12.8, "text": " This will be an algorithm called forward propagation."}, {"start"... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=VctGI7Xaogw | 4.8 TensorFlow implementation | Inference in Code --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | TensorFlow is one of the leading frameworks for implementing deep learning algorithms. When I'm building projects, TensorFlow is actually the tool that I use the most often and the other popular tool is PyTorch. But we're going to focus in this specialization on TensorFlow. In this video, let's take a look at how you ... | [{"start": 0.0, "end": 6.22, "text": " TensorFlow is one of the leading frameworks for implementing deep learning algorithms."}, {"start": 6.22, "end": 10.86, "text": " When I'm building projects, TensorFlow is actually the tool that I use the most often"}, {"start": 10.86, "end": 13.94, "text": " and the other popular... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Q3cLU1trK_E | 4.9 TensorFlow implementation | Data in TensorFlow --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, I want to step through with you how data is represented in NumPy and IntensiveLow so that as you're implementing new neural networks, you can have a consistent framework to think about how to represent your data. One of the unfortunate things about the way things are done in code today is that many, man... | [{"start": 0.0, "end": 9.16, "text": " In this video, I want to step through with you how data is represented in NumPy and IntensiveLow"}, {"start": 9.16, "end": 15.0, "text": " so that as you're implementing new neural networks, you can have a consistent framework"}, {"start": 15.0, "end": 18.6, "text": " to think abo... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=8GAQXo6SRws | 4.10 TensorFlow implementation | Building a neural network --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So you've seen a bunch of TensorFlow code by now, learned about how to build a layer in TensorFlow, how to do forward prop through a single layer in TensorFlow, and also learned about data in TensorFlow. Let's put it all together and talk about how to build a neural network in TensorFlow. This is also the last video o... | [{"start": 0.0, "end": 5.6000000000000005, "text": " So you've seen a bunch of TensorFlow code by now, learned about how to build a layer"}, {"start": 5.6000000000000005, "end": 11.28, "text": " in TensorFlow, how to do forward prop through a single layer in TensorFlow, and also learned"}, {"start": 11.28, "end": 13.88... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ydKVT95-Ufo | 4.11 Neural network implementation in Python | Forward prop in a single layer-[ML| Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | If you had to implement forward propagation yourself from scratch in Python, how would you go about doing so? In addition to gaining intuition about what's really going on in libraries like TensorFlow and PyTorch, if ever someday you decide you want to build something even better than TensorFlow and PyTorch, maybe now... | [{"start": 0.0, "end": 7.6000000000000005, "text": " If you had to implement forward propagation yourself from scratch in Python, how would"}, {"start": 7.6000000000000005, "end": 9.72, "text": " you go about doing so?"}, {"start": 9.72, "end": 14.8, "text": " In addition to gaining intuition about what's really going ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=snDJExVtMMQ | 4.12 Neural network implementation in Python | General implementation of forward propagation-ML Ng | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw how to implement forward prop in Python, but by hard coding lines of code for every single neuron. Let's now take a look at the more general implementation of forward prop in Python. Similar to the previous video, my goal in this video is to show you the code so that when you see it again in... | [{"start": 0.0, "end": 7.96, "text": " In the last video, you saw how to implement forward prop in Python, but by hard coding"}, {"start": 7.96, "end": 10.88, "text": " lines of code for every single neuron."}, {"start": 10.88, "end": 15.88, "text": " Let's now take a look at the more general implementation of forward ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=nxf7N2ZMdlI | 4.13 Speculations on artificial general intellignece (AGI) | Is there a path to AGI ? - ML Andrew Ng | None | Ever since I was a teenager, starting to play around with neural networks, I always felt that the dream of maybe someday building an AI system that's as intelligent as myself or as intelligent as a typical human, that that was one of the most inspiring dreams of AI. I still hold that dream alive today, but I think tha... | [{"start": 0.0, "end": 6.24, "text": " Ever since I was a teenager, starting to play around with neural networks, I always felt"}, {"start": 6.24, "end": 11.84, "text": " that the dream of maybe someday building an AI system that's as intelligent as myself"}, {"start": 11.84, "end": 16.68, "text": " or as intelligent a... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=3iDMb-EUQPA | 4.14 Vectorization (optional) | How neural networks are implemented efficiently-- [ML- Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | One of the reasons that deep learning researchers have been able to scale up neural networks and build really large neural networks over the last decade is because neural networks can be vectorized. They can be implemented very efficiently using matrix multiplications. And it turns out that parallel computing hardware... | [{"start": 0.0, "end": 7.16, "text": " One of the reasons that deep learning researchers have been able to scale up neural networks"}, {"start": 7.16, "end": 12.24, "text": " and build really large neural networks over the last decade is because neural networks"}, {"start": 12.24, "end": 14.16, "text": " can be vectori... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=CYnwhHKnuwY | 4.15 Vectorization (optional) | Matrix multiplication-- [Machine Learning - Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So you know that a matrix is just a block or 2D array of numbers. What does it mean to multiply two matrices? Let's take a look. In order to build up to multiplying matrices, let's start by looking at how we take dot products between vectors. Let's use the example of taking the dot product between this vector 1, 2, an... | [{"start": 0.0, "end": 8.0, "text": " So you know that a matrix is just a block or 2D array of numbers."}, {"start": 8.0, "end": 11.200000000000001, "text": " What does it mean to multiply two matrices?"}, {"start": 11.200000000000001, "end": 12.72, "text": " Let's take a look."}, {"start": 12.72, "end": 19.76, "text":... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=7GFKFng9gyM | 4.16 Vectorization (optional) | Matrix multiplication rules-- [Machine Learning - Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, let's take a look at the general form of how you multiply two matrices together. And then in the last video after this one, we'll take this and apply it to the vectorized implementation of a neural network. Let's dive in. Here's a matrix A, which is a two by three matrix, because it has two rows and three columns.... | [{"start": 0.0, "end": 8.6, "text": " So, let's take a look at the general form of how you multiply two matrices together."}, {"start": 8.6, "end": 13.92, "text": " And then in the last video after this one, we'll take this and apply it to the vectorized"}, {"start": 13.92, "end": 16.68, "text": " implementation of a n... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=m7xcF9jXLpc | 4.17 Vectorization (optional) | Matrix multiplication code-- [Machine Learning - Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So, without further ado, let's jump into the vectorized implementation of a neural network. We'll look at the code that you have seen in an earlier video, and hopefully, MatMul, that is that matrix multiplication calculation, will make more sense. Let's jump in. So you saw previously how you can take the matrix A and ... | [{"start": 0.0, "end": 7.28, "text": " So, without further ado, let's jump into the vectorized implementation of a neural network."}, {"start": 7.28, "end": 12.120000000000001, "text": " We'll look at the code that you have seen in an earlier video, and hopefully, MatMul,"}, {"start": 12.120000000000001, "end": 16.28, ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=jdjGdT_jR50 | 5.1 Neural Network Training | TensorFlow implementation --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome back to the second week of this course on advanced learning algorithms. Last week you learned how to carry out inference in a neural network. This week we're going to go over training of a neural network. I think being able to take your own data and train your own neural network on it is really fun. This week ... | [{"start": 0.0, "end": 7.640000000000001, "text": " Welcome back to the second week of this course on advanced learning algorithms."}, {"start": 7.640000000000001, "end": 11.76, "text": " Last week you learned how to carry out inference in a neural network."}, {"start": 11.76, "end": 15.76, "text": " This week we're go... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=jzaF4An03Oc | 5.2 Neural Network Training | Training Details --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's take a look at the details of what the TensorFlow code for training a neural network is actually doing. Let's dive in. Before looking at the details of training a neural network, let's recall how you had trained a logistic regression model in the previous course. Step 1 of building a logistic regression model wa... | [{"start": 0.0, "end": 6.96, "text": " Let's take a look at the details of what the TensorFlow code for training a neural network"}, {"start": 6.96, "end": 7.96, "text": " is actually doing."}, {"start": 7.96, "end": 9.64, "text": " Let's dive in."}, {"start": 9.64, "end": 14.58, "text": " Before looking at the details... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=TQ388ISSoI4 | 5.3 Activation Functions | Alternatives to the sigmoid activation --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So far, we've been using the sigmoid activation function in all the nodes, in the hidden layers, and in the output layer. And we have started that way because we were building up neural networks by taking logistic regression and creating a lot of logistic regression units and stringing them together. But if you use ot... | [{"start": 0.0, "end": 8.76, "text": " So far, we've been using the sigmoid activation function in all the nodes, in the hidden layers,"}, {"start": 8.76, "end": 11.08, "text": " and in the output layer."}, {"start": 11.08, "end": 16.28, "text": " And we have started that way because we were building up neural networks... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=orElYjWScBw | 5.4 Activation Functions | Choosing activation functions--[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's take a look at how you can choose the activation function for different neurons in your neural network. We'll start with some guidance for how to choose it for the output layer. It turns out that depending on what the target label or the ground truth label Y is, there will be one fairly natural choice for the ac... | [{"start": 0.0, "end": 6.3, "text": " Let's take a look at how you can choose the activation function for different neurons"}, {"start": 6.3, "end": 7.84, "text": " in your neural network."}, {"start": 7.84, "end": 12.42, "text": " We'll start with some guidance for how to choose it for the output layer."}, {"start": 1... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=fK5YzGIc2u8 | 5.5 Activation Functions | Why do we need activation functions? --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's take a look at why neural networks need activation functions and why they just don't work if we were to use the linear activation function in every neuron in the neural network. Recall this demand prediction example. What would happen if we were to use a linear activation function for all of the nodes in this ne... | [{"start": 0.0, "end": 8.700000000000001, "text": " Let's take a look at why neural networks need activation functions and why they just don't"}, {"start": 8.700000000000001, "end": 15.860000000000001, "text": " work if we were to use the linear activation function in every neuron in the neural network."}, {"start": 15... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=hfskCwks_X8 | 5.6 Multiclass Classification | Multiclass --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Multi-class classification refers to classification problems where you can have more than just two possible output labels, so not just zero or one. Let's take a look at what that means. For the handwritten digit classification problems we've looked at so far, we were just trying to distinguish between the handwritten ... | [{"start": 0.0, "end": 7.96, "text": " Multi-class classification refers to classification problems where you can have more than just"}, {"start": 7.96, "end": 13.52, "text": " two possible output labels, so not just zero or one."}, {"start": 13.52, "end": 15.74, "text": " Let's take a look at what that means."}, {"sta... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=pzxxgEZkdLM | 5.7 Multiclass Classification | Softmax --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The softmax regression algorithm is a generalization of logistic regression, which is a binary classification algorithm to the multi-cost classification context. Let's take a look at how it works. Recall that logistic regression applies when y can take on two possible output values, either 0 or 1. And the way it compu... | [{"start": 0.0, "end": 8.48, "text": " The softmax regression algorithm is a generalization of logistic regression, which is a binary"}, {"start": 8.48, "end": 13.0, "text": " classification algorithm to the multi-cost classification context."}, {"start": 13.0, "end": 16.2, "text": " Let's take a look at how it works."... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=24QO9iNXvWs | 5.8 Multiclass Classification | Neural Network with Softmax output --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In order to build a neural network that can carry out multi-class classification, we're going to take the Softmax regression model and put it into essentially the output layer of a neural network. Let's take a look at how to do that. Previously, when we were doing handwritten digit recognition with just two clauses, w... | [{"start": 0.0, "end": 7.12, "text": " In order to build a neural network that can carry out multi-class classification, we're"}, {"start": 7.12, "end": 13.24, "text": " going to take the Softmax regression model and put it into essentially the output layer"}, {"start": 13.24, "end": 14.24, "text": " of a neural networ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Izt9Bn8HLUM | 5.9 Multiclass Classification | Improved implementation of softmax --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The implementation that you saw in the last video of a neural network with a softmax layer will work okay, but there's an even better way to implement it. Let's take a look at what can go wrong with that implementation and also how to make it better. Let me show you two different ways of computing the same quantity in... | [{"start": 0.0, "end": 8.92, "text": " The implementation that you saw in the last video of a neural network with a softmax layer"}, {"start": 8.92, "end": 13.68, "text": " will work okay, but there's an even better way to implement it."}, {"start": 13.68, "end": 18.04, "text": " Let's take a look at what can go wrong ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=1yv8_S9Srcg | 5.10 Mult-label Classification | Classification with multiple outputs (Optional) --[ML | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | You've learned about multi-class classification, where the output label y can be any one of two or potentially many more than two possible categories. There's a different type of classification problem called a multi-label classification problem, which is where, associated with each image, there could be multiple labe... | [{"start": 0.0, "end": 9.28, "text": " You've learned about multi-class classification, where the output label y can be any one of"}, {"start": 9.28, "end": 13.280000000000001, "text": " two or potentially many more than two possible categories."}, {"start": 13.280000000000001, "end": 19.2, "text": " There's a differen... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=yo6aW-D7sCM | 5.11 Additional Neural Network Concepts | Advanced Optimization --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Gradient descent is an optimization algorithm that is widely used in machine learning and was the foundation of many algorithms like linear regression and logistic regression and early implementations of neural networks. But it turns out that there are now some other optimization algorithms for minimizing the cost fun... | [{"start": 0.0, "end": 8.8, "text": " Gradient descent is an optimization algorithm that is widely used in machine learning and"}, {"start": 8.8, "end": 14.56, "text": " was the foundation of many algorithms like linear regression and logistic regression"}, {"start": 14.56, "end": 18.56, "text": " and early implementat... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=54TxZZpK5Ok | 5.12 Additional Neural Network Concepts | Additional Layer Types --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | All the neural network layers we've used so far have been the dense layer type in which every neuron in a layer gets as its inputs all the activations from the previous layer. And it turns out that just using the dense layer type, you can actually build some pretty powerful learning algorithms. And to help you build f... | [{"start": 0.0, "end": 8.6, "text": " All the neural network layers we've used so far have been the dense layer type in which"}, {"start": 8.6, "end": 15.58, "text": " every neuron in a layer gets as its inputs all the activations from the previous layer."}, {"start": 15.58, "end": 20.16, "text": " And it turns out tha... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Y66jLs9ubsY | 6.1 Advice for applying machine learning | Deciding what to try next -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Hi and welcome back. By now you've seen a lot of different learning algorithms including linear regression, logistic regression, even deep learning or neural networks, and next week you'll see decision trees as well. So you now have a lot of powerful tools of machine learning, but how do you use these tools effectivel... | [{"start": 0.0, "end": 4.0600000000000005, "text": " Hi and welcome back."}, {"start": 4.0600000000000005, "end": 8.8, "text": " By now you've seen a lot of different learning algorithms including linear regression, logistic"}, {"start": 8.8, "end": 14.200000000000001, "text": " regression, even deep learning or neural... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=uNNx1Czrt1w | 6.2 Evaluating and choosing models | Evaluating a model -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's say you've trained a machine learning model. How do you evaluate that model's performance? You find that having a systematic way to evaluate performance will also help paint a clearer path for how to then improve this performance. So let's take a look at how to evaluate a model. Let's take the example of learnin... | [{"start": 0.0, "end": 4.48, "text": " Let's say you've trained a machine learning model."}, {"start": 4.48, "end": 6.88, "text": " How do you evaluate that model's performance?"}, {"start": 6.88, "end": 12.040000000000001, "text": " You find that having a systematic way to evaluate performance will also help paint a c... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=KwM_IYQ_I-8 | 6.3 Evaluating and choosing models | Model selection and training/cross validation/test sets-ML Ng | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the last video, you saw how to use a test set to evaluate the performance of a model. Let's make one further refinement to that idea in this video, which will allow you to use a technique to automatically choose a good model for your machine learning algorithm. One thing we've seen is that once the model's paramete... | [{"start": 0.0, "end": 7.68, "text": " In the last video, you saw how to use a test set to evaluate the performance of a model."}, {"start": 7.68, "end": 11.8, "text": " Let's make one further refinement to that idea in this video, which will allow you to"}, {"start": 11.8, "end": 17.34, "text": " use a technique to au... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=YB61HDL7EzE | 6.4 Bias and variance | Diagnosing bias and variance -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The typical workflow of developing a machine learning system is that you have an idea and you train a model and you almost always find that it doesn't work as well as you wish yet. When I'm training machine learning model, it pretty much never works that well the first time. And so key to the process of building machi... | [{"start": 0.0, "end": 8.2, "text": " The typical workflow of developing a machine learning system is that you have an idea and"}, {"start": 8.2, "end": 14.200000000000001, "text": " you train a model and you almost always find that it doesn't work as well as you wish yet."}, {"start": 14.200000000000001, "end": 17.92,... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=2Ji4Upc606c | 6.5 Bias and variance | Regularization and bias/variance -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | You saw in the last video how different choices of the degree of polynomial d affects the bias and variance of your learning algorithm and therefore its overall performance. In this video, let's take a look at how regularization, specifically the choice of the regularization parameter lambda, affects the bias and vari... | [{"start": 0.0, "end": 8.44, "text": " You saw in the last video how different choices of the degree of polynomial d affects the"}, {"start": 8.44, "end": 13.36, "text": " bias and variance of your learning algorithm and therefore its overall performance."}, {"start": 13.36, "end": 18.84, "text": " In this video, let's... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=8Rl_2WQbmlc | 6.6 Bias and variance |Establishing a baseline level of performance -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's look at some concrete numbers for what JTrain and JCV might be and see how you can judge if a learning algorithm has high bias or high variance. And for the examples in this video, I'm going to use as a running example the application of speech recognition, which is something I've worked on multiple times over t... | [{"start": 0.0, "end": 8.0, "text": " Let's look at some concrete numbers for what JTrain and JCV might be and see how you can"}, {"start": 8.0, "end": 12.280000000000001, "text": " judge if a learning algorithm has high bias or high variance."}, {"start": 12.280000000000001, "end": 17.36, "text": " And for the example... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=m0QgVaFS6O4 | 6.7 Bias and variance | Learning curves --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Learning curves are a way to help understand how your learning algorithm is doing as a function of the amount of experience it has, whereby experience I mean, for example, the number of training examples it has. Let's take a look. Let me plot learning curves for a model that fits a second order polynomial quadratic fu... | [{"start": 0.0, "end": 9.4, "text": " Learning curves are a way to help understand how your learning algorithm is doing as a function of the amount of experience it has,"}, {"start": 9.4, "end": 14.0, "text": " whereby experience I mean, for example, the number of training examples it has."}, {"start": 14.0, "end": 22.... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=00gn1isOd70 | 6.8 Bias and variance | Deciding what to try next revisited --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | You've seen how by looking at JTrain and JCV, that is the training error and the cross-validation error, or maybe even plotting a learning curve, you can try to get a sense of whether your learning algorithm has high bias or high variance. This is a procedure I routinely do when I'm training a learning algorithm. I'll... | [{"start": 0.0, "end": 8.56, "text": " You've seen how by looking at JTrain and JCV, that is the training error and the cross-validation"}, {"start": 8.56, "end": 14.0, "text": " error, or maybe even plotting a learning curve, you can try to get a sense of whether your"}, {"start": 14.0, "end": 17.68, "text": " learnin... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Hso8Qq3-arc | 6.9 Bias and variance | Bias/variance and neural networks --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | We've seen that high bias or high variance are both bad in the sense that they hurt the performance of your algorithm. One of the reasons that neural networks have been so successful is because neural networks, together with the idea of big data or hopefully having large datasets, is giving us a new way of new ways to... | [{"start": 0.0, "end": 6.4, "text": " We've seen that high bias or high variance are both bad in the sense that they hurt the"}, {"start": 6.4, "end": 8.88, "text": " performance of your algorithm."}, {"start": 8.88, "end": 14.0, "text": " One of the reasons that neural networks have been so successful is because neura... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=CndPUzZpZ38 | 6.10 Machine Learning development process | Iterative loop of ML development --[ML | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the next few videos, I'd like to share with you what it's like to go through the process of developing a machine learning system so that when you are doing so yourself, hopefully you'll be in a position to make great decisions at many stages of the machine learning development process. Let's take a look first at th... | [{"start": 0.0, "end": 7.0, "text": " In the next few videos, I'd like to share with you what it's like to go through the"}, {"start": 7.0, "end": 12.8, "text": " process of developing a machine learning system so that when you are doing so yourself, hopefully"}, {"start": 12.8, "end": 18.32, "text": " you'll be in a p... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=-EKKWG6CXRQ | 6.11 Machine Learning development process | Error analysis --[ML | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In terms of the most important ways to help you run diagnostics to choose what to try next to improve your learning algorithm performance, I would say bias invariance is probably the most important idea and error analysis would probably be second on my list. So let's take a look at what this means. Concretely, let's s... | [{"start": 0.0, "end": 7.5200000000000005, "text": " In terms of the most important ways to help you run diagnostics to choose what to try"}, {"start": 7.5200000000000005, "end": 12.76, "text": " next to improve your learning algorithm performance, I would say bias invariance is probably the"}, {"start": 12.76, "end": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=YQNWuC69uqI | 6.12 Machine Learning development process | Adding data --[ML | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, I'd like to share with you some tips for adding data or collecting more data or sometimes even creating more data for your machine learning application. Just a heads up that this and the next few videos will seem a little bit like a grab bag of different techniques and I apologize if it seems a little b... | [{"start": 0.0, "end": 7.96, "text": " In this video, I'd like to share with you some tips for adding data or collecting more"}, {"start": 7.96, "end": 13.24, "text": " data or sometimes even creating more data for your machine learning application."}, {"start": 13.24, "end": 17.56, "text": " Just a heads up that this ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=QAZroxyW4oo | 6.13 Machine Learning development process |Transfer learning using data from a different task -ML Ng | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | For an application where you don't have that much data, transfer learning is a wonderful technique that lets you use data from a different task to help on your application. This is one of those techniques that I use very frequently. Let's take a look at how transfer learning works. Here's how transfer learning works. ... | [{"start": 0.0, "end": 5.68, "text": " For an application where you don't have that much data,"}, {"start": 5.68, "end": 8.92, "text": " transfer learning is a wonderful technique that lets you use"}, {"start": 8.92, "end": 13.02, "text": " data from a different task to help on your application."}, {"start": 13.02, "en... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ekahwgWO5PQ | 6.14 Machine Learning development process | Full cycle of a machine learning project -[ML|Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So far, we've talked a lot about how to train a model and also talked a bit about how to get data for your machine learning application. But when I'm building a machine learning system, I find that training a model is just part of the puzzle. In this video, I'd like to share with you what I think of as the full cycle ... | [{"start": 0.0, "end": 7.640000000000001, "text": " So far, we've talked a lot about how to train a model and also talked a bit about how to"}, {"start": 7.640000000000001, "end": 11.32, "text": " get data for your machine learning application."}, {"start": 11.32, "end": 16.64, "text": " But when I'm building a machine... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=9ftZArU_8BA | 6.15 Machine Learning development process | Fairness, bias, and ethics -[Machine Learning|Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Machine learning algorithms today are affecting billions of people. You've heard me mention ethics in other videos before, and I hope that if you're building a machine learning system that affects people, that you give some thought to making sure that your system is reasonably fair, reasonably free from bias, and that... | [{"start": 0.0, "end": 6.640000000000001, "text": " Machine learning algorithms today are affecting billions of people."}, {"start": 6.640000000000001, "end": 11.94, "text": " You've heard me mention ethics in other videos before, and I hope that if you're building"}, {"start": 11.94, "end": 17.240000000000002, "text":... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=WiaW5w3CFyM | 6.16 Skewed datasets (optional) | Error metrics for skewed datasets -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | If you're working on a machine learning application where the ratio of positive to negative examples is very skewed, very far from 50-50, then it turns out that the usual error metrics like accuracy don't work that well. Let's start with an example. Let's say you're training a binary classifier to detect a rare diseas... | [{"start": 0.0, "end": 7.5600000000000005, "text": " If you're working on a machine learning application where the ratio of positive to negative examples"}, {"start": 7.5600000000000005, "end": 13.620000000000001, "text": " is very skewed, very far from 50-50, then it turns out that the usual error metrics like"}, {"st... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=JhPHwCMpyO8 | 6.17 Skewed datasets (optional) | Trading off precision and recall -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the IDU case, we like for learning algorithms to have high precision and high recall. High precision would mean that if it diagnoses a patient with that rare disease, probably the patient does have it and is in accurate diagnosis. And high recall means that if there's a patient with that rare disease, probably the ... | [{"start": 0.0, "end": 6.92, "text": " In the IDU case, we like for learning algorithms to have high precision and high recall."}, {"start": 6.92, "end": 11.96, "text": " High precision would mean that if it diagnoses a patient with that rare disease, probably"}, {"start": 11.96, "end": 15.36, "text": " the patient doe... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=HkIEEOhnwEs | 7.1 Decision Trees | Decision Tree Model -[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Welcome to the final week of this course on advanced learning algorithms. One of the learning algorithms that's very powerful, widely used in many applications, also used by many to win machine learning competitions is decision trees and tree ensembles. Despite all the successes of decision trees, they somehow haven't... | [{"start": 0.0, "end": 6.8, "text": " Welcome to the final week of this course on advanced learning algorithms."}, {"start": 6.8, "end": 11.64, "text": " One of the learning algorithms that's very powerful, widely used in many applications,"}, {"start": 11.64, "end": 18.52, "text": " also used by many to win machine le... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=AtGze6G7GV8 | 7.2 Decision Trees | Learning Process --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The process of building a decision tree, given a training set, has a few steps. In this video, let's take a look at the overall process of what you need to do to build a decision tree. Given a training set of 10 examples of cats and dogs, like you saw in the last video, the first step of decision tree learning is we h... | [{"start": 0.0, "end": 6.96, "text": " The process of building a decision tree, given a training set, has a few steps."}, {"start": 6.96, "end": 11.48, "text": " In this video, let's take a look at the overall process of what you need to do to build a"}, {"start": 11.48, "end": 13.36, "text": " decision tree."}, {"star... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=eRUK9CEmOcI | 7.3 Decision Tree Learning | Measuring purity --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In this video, we'll look at a way of measuring the purity of a set of examples. If the examples are all cats or single class, then it's very pure. If it's all not cats, that's also very pure. But if it's somewhere in between, how do you quantify how pure is this set of examples? Let's take a look at the definition of... | [{"start": 0.0, "end": 8.040000000000001, "text": " In this video, we'll look at a way of measuring the purity of a set of examples."}, {"start": 8.040000000000001, "end": 11.68, "text": " If the examples are all cats or single class, then it's very pure."}, {"start": 11.68, "end": 15.6, "text": " If it's all not cats,... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=pZ9uGPkqolc | 7.4 Decision Tree Learning | Choosing a split : Information Gain --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | When building a decision tree, the way we'll decide what feature to split on at a node will be based on what choice of feature reduces entropy the most. Reduces entropy or reduces impurity or maximizes purity. In decision tree learning, the reduction of entropy is called information gain. Let's take a look in this vid... | [{"start": 0.0, "end": 7.3, "text": " When building a decision tree, the way we'll decide what feature to split on at a node"}, {"start": 7.3, "end": 12.56, "text": " will be based on what choice of feature reduces entropy the most."}, {"start": 12.56, "end": 17.44, "text": " Reduces entropy or reduces impurity or maxi... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=8Gc2AfeP5yE | 7.5 Decision Tree Learning | Putting it together --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | The information gain criteria lets you decide how to choose one feature to split at one node. Let's take that and use that in multiple places through a decision tree in order to figure out how to build a large decision tree with multiple nodes. Here's the overall process of building a decision tree. Start with all tra... | [{"start": 0.0, "end": 6.88, "text": " The information gain criteria lets you decide how to choose one feature to split at one"}, {"start": 6.88, "end": 7.88, "text": " node."}, {"start": 7.88, "end": 12.72, "text": " Let's take that and use that in multiple places through a decision tree in order to figure"}, {"start"... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=y_VfAJG6H-Q | 7.6 Decision Tree Learning | Using one-hot encoding of categorical features --[ML | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In the example we've seen so far, each of the features could take on only one of two possible values. The ear shape was either pointy or floppy, the face shape was either round or not round, and whiskers were either present or absent. But what if you have features that can take on more than two discrete values? In thi... | [{"start": 0.0, "end": 7.5200000000000005, "text": " In the example we've seen so far, each of the features could take on only one of two"}, {"start": 7.5200000000000005, "end": 9.32, "text": " possible values."}, {"start": 9.32, "end": 14.32, "text": " The ear shape was either pointy or floppy, the face shape was eith... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=oK5pBru6Tfo | 7.7 Decision Tree Learning | Continuous valued features --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Let's look at how you can modify a decision tree to work with features that aren't just discrete value but continuous value, that is features that can be any number. Let's start with an example. I have modified the cat adoption center of datasets to add one more feature, which is the weight of the animal in pounds. On... | [{"start": 0.0, "end": 3.96, "text": " Let's look at how you can modify a decision tree to work with"}, {"start": 3.96, "end": 7.5, "text": " features that aren't just discrete value but continuous value,"}, {"start": 7.5, "end": 10.16, "text": " that is features that can be any number."}, {"start": 10.16, "end": 12.12... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=0ha0UEJJydU | 7.8 Decision Tree Learning | Regression Trees (optional) --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | So far, we've only been talking about decision trees as classification algorithms. In this optional video, we'll generalize decision trees to be regression algorithms so that they can predict a number. Let's take a look. The example I'm going to use for this video will be to use the discrete value features that we had... | [{"start": 0.0, "end": 7.4, "text": " So far, we've only been talking about decision trees as classification algorithms."}, {"start": 7.4, "end": 12.68, "text": " In this optional video, we'll generalize decision trees to be regression algorithms so that"}, {"start": 12.68, "end": 14.46, "text": " they can predict a nu... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=yoYgcddMXrg | 7.9 Tree ensembles | Using multiple decision trees --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | One of the weaknesses of using a single decision tree is that that decision tree can be highly sensitive to small changes in the data. And one solution to make the algorithm less sensitive or more robust is to build not one decision tree but to build a lot of decision trees and we call that a tree ensemble. Let's take... | [{"start": 0.0, "end": 7.48, "text": " One of the weaknesses of using a single decision tree is that that decision tree can be highly"}, {"start": 7.48, "end": 11.36, "text": " sensitive to small changes in the data."}, {"start": 11.36, "end": 16.7, "text": " And one solution to make the algorithm less sensitive or mor... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=dELaA7ZykMs | 7.10 Tree ensembles | Sampling with replacement --[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | In order to build a tree ensemble, we're going to need a technique called sampling with replacement. Let's take a look at what that means. In order to illustrate how sampling with replacement works, I'm going to show you a demonstration of sampling with replacement using four tokens that are colored red, yellow, green... | [{"start": 0.0, "end": 8.88, "text": " In order to build a tree ensemble, we're going to need a technique called sampling with replacement."}, {"start": 8.88, "end": 11.200000000000001, "text": " Let's take a look at what that means."}, {"start": 11.200000000000001, "end": 17.36, "text": " In order to illustrate how sa... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=VfKwWWrSnq4 | 7.11 Tree ensembles | Random forest algorithm--[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Now that we have a way to use Soundplay with Replacement to create new training sets that are a bit similar to but also quite different from the original training set, we're ready to build our first tree ensemble algorithm. In particular, in this video, we'll talk about the random forest algorithm, which is one powerf... | [{"start": 0.0, "end": 7.4, "text": " Now that we have a way to use Soundplay with Replacement to create new training sets that"}, {"start": 7.4, "end": 12.14, "text": " are a bit similar to but also quite different from the original training set, we're ready"}, {"start": 12.14, "end": 16.1, "text": " to build our firs... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=4e4BTYrA0IE | 7.12 Tree ensembles | XGBoost--[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Over the years, machine learning researchers have come up with a lot of different ways to build decision trees and decision tree ensembles. Today, by far the most commonly used way or implementation of decision tree ensembles or decision trees is an algorithm called XGBoost. It runs quickly, the open source implementa... | [{"start": 0.0, "end": 5.86, "text": " Over the years, machine learning researchers have come up with a lot of different ways"}, {"start": 5.86, "end": 9.3, "text": " to build decision trees and decision tree ensembles."}, {"start": 9.3, "end": 15.540000000000001, "text": " Today, by far the most commonly used way or i... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=ckbQPwQ6y98 | 7.13 Conclusion | When to use decision trees--[Machine Learning | Andrew Ng] | Second Course:
Advanced Learning Algorithms.
If you liked the content please subscribe and put a little blue thumb.
Take heart! | Both decision trees, including tree ensembles, as well as neural networks, are very powerful, very effective learning algorithms. When should you pick one or the other? Let's look at some of the pros and cons of each. Decision trees and tree ensembles will often work well on tabular data, also called structured data. ... | [{"start": 0.0, "end": 8.0, "text": " Both decision trees, including tree ensembles, as well as neural networks, are very powerful,"}, {"start": 8.0, "end": 10.200000000000001, "text": " very effective learning algorithms."}, {"start": 10.200000000000001, "end": 12.68, "text": " When should you pick one or the other?"}... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.