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Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=faTk41hUGec | 8.1 Unsupervised Learning, Recommenders, Reinforcement Learning|Welcome! -Machine Learning Andrew Ng | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Welcome to this third and final course of this specialization on unsupervised learning, recommender systems, and reinforcement learning. Whereas in the first two courses, we spent a lot of time on supervised learning, in this third and final course, we'll talk about a new set of techniques that goes beyond supervised ... | [{"start": 0.0, "end": 8.2, "text": " Welcome to this third and final course of this specialization on unsupervised learning,"}, {"start": 8.2, "end": 11.120000000000001, "text": " recommender systems, and reinforcement learning."}, {"start": 11.120000000000001, "end": 16.32, "text": " Whereas in the first two courses,... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=P9ma8vycu3Y | 8.2 Clustering | What is clustering? -- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | What is clustering? A clustering algorithm looks at a number of data points and automatically finds data points that are related or similar to each other. Let's take a look at what that means. Let me contrast clustering, which is an unsupervised learning algorithm, with what you had previously seen with supervised lea... | [{"start": 0.0, "end": 3.7600000000000002, "text": " What is clustering?"}, {"start": 3.7600000000000002, "end": 9.68, "text": " A clustering algorithm looks at a number of data points and automatically finds data points"}, {"start": 9.68, "end": 12.68, "text": " that are related or similar to each other."}, {"start": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Q9tKUSAO2LY | 8.3 Clustering | K-means intuition-- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Let's take a look at what the k-means clustering algorithm does. Let me start with an example. Here I've plotted a dataset with 30 unlabeled training examples. So there are 30 points. And what we'd like to do is run k-means on this dataset. The first thing that the k-means algorithm does is it will take a random guess... | [{"start": 0.0, "end": 5.8, "text": " Let's take a look at what the k-means clustering algorithm does."}, {"start": 5.8, "end": 8.6, "text": " Let me start with an example."}, {"start": 8.6, "end": 13.58, "text": " Here I've plotted a dataset with 30 unlabeled training examples."}, {"start": 13.58, "end": 15.56, "text"... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=wb5tJ4Hw27A | 8.4 Clustering | K-means algorithm-- [Machine Learning | Andrew Ng] | None | In the last video you saw an illustration of the k-means algorithm running. Now let's write out the k-means algorithm in detail so that you'd be able to implement it for yourself. Here's the k-means algorithm. The first step is to randomly initialize k cluster centroid mu1, mu2 through mu k. In the example that we had... | [{"start": 0.0, "end": 7.32, "text": " In the last video you saw an illustration of the k-means algorithm running."}, {"start": 7.32, "end": 11.64, "text": " Now let's write out the k-means algorithm in detail so that you'd be able to implement"}, {"start": 11.64, "end": 12.64, "text": " it for yourself."}, {"start": 1... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=SSoA7w8HvK8 | 8.5 Clustering | Optimization objective-- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In the earlier courses, courses one and two of the specialization, you saw a lot of supervised learning algorithms as ticket training set, posing a cost function, and then using gradient descent or some other algorithms to optimize that cost function. It turns out that the Q means algorithm that you saw in the last vi... | [{"start": 0.0, "end": 7.84, "text": " In the earlier courses, courses one and two of the specialization, you saw a lot of supervised"}, {"start": 7.84, "end": 14.52, "text": " learning algorithms as ticket training set, posing a cost function, and then using gradient"}, {"start": 14.52, "end": 18.56, "text": " descent... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=qCPYJL_tQK8 | 8.6 Clustering | Initializing K-means-- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | The very first step of the k-means clustering algorithm was to choose random locations as the initial guesses for the cluster centroids mu1 through mu k. But how do you actually take that random guess? Let's take a look at that in this video, as well as how you can take multiple attempts at the initial guesses for mu1... | [{"start": 0.0, "end": 7.44, "text": " The very first step of the k-means clustering algorithm was to choose random locations as"}, {"start": 7.44, "end": 12.280000000000001, "text": " the initial guesses for the cluster centroids mu1 through mu k."}, {"start": 12.280000000000001, "end": 15.72, "text": " But how do you... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=3OaUVZbeYgA | 8.7 Clustering | Choosing the number of clusters -- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | The k-means algorithm requires as one of its inputs k, the number of clusters you want it to find. But how do you decide how many clusters to use? Do you want two clusters or three clusters or five clusters or ten clusters? Let's take a look. For a lot of clustering problems, the right value of k is truly ambiguous. I... | [{"start": 0.0, "end": 7.96, "text": " The k-means algorithm requires as one of its inputs k, the number of clusters you want it"}, {"start": 7.96, "end": 8.96, "text": " to find."}, {"start": 8.96, "end": 11.96, "text": " But how do you decide how many clusters to use?"}, {"start": 11.96, "end": 15.88, "text": " Do yo... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=e6oYV5MDXFk | 8.8 Anomaly Detection | Finding unusual events -- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Let's look at our second unsupervised learning algorithm. Anomaly detection algorithms look at an unlabeled data set of normal events and thereby learns to detect or to raise a red flag for if there is an unusual or an anomalous event. Let's look at an example. Some of my friends were working on using anomaly detectio... | [{"start": 0.0, "end": 6.96, "text": " Let's look at our second unsupervised learning algorithm."}, {"start": 6.96, "end": 13.8, "text": " Anomaly detection algorithms look at an unlabeled data set of normal events and thereby learns"}, {"start": 13.8, "end": 20.72, "text": " to detect or to raise a red flag for if the... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=kAVXR1qGjwU | 8.9 Anomaly Detection | Gaussian (normal) distribution -- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In order to apply anomaly detection, we're going to need to use the Gaussian distribution, which is also called the normal distribution. So when you hear me say either Gaussian distribution or normal distribution, they mean exactly the same thing. And if you've heard of the bell-shaped distribution, that also refers t... | [{"start": 0.0, "end": 8.84, "text": " In order to apply anomaly detection, we're going to need to use the Gaussian distribution,"}, {"start": 8.84, "end": 12.46, "text": " which is also called the normal distribution."}, {"start": 12.46, "end": 17.900000000000002, "text": " So when you hear me say either Gaussian dist... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=mAtCVwNeeQU | 8.10 Anomaly Detection | Anomaly detection algorithm-- [Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Now that you've seen how the Gaussian or the normal distribution works for a single number, we're ready to build our anomaly detection algorithm. Let's dive in. You have a training set X1 through Xm where here each example X has n features. So each example X is a vector with n numbers. In the case of the airplane engi... | [{"start": 0.0, "end": 7.82, "text": " Now that you've seen how the Gaussian or the normal distribution works for a single number,"}, {"start": 7.82, "end": 10.96, "text": " we're ready to build our anomaly detection algorithm."}, {"start": 10.96, "end": 12.56, "text": " Let's dive in."}, {"start": 12.56, "end": 20.96,... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=LtgmlBfZAz0 | 8.11 Anomaly Detection | Developing and evaluating an anomaly detection system-- [ML | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | I'd like to share with you some practical tips for developing an anomaly detection system. One of the key ideas will be that if you can have a way to evaluate a system even as it's being developed, you'll be able to make decisions and change a system and improve it much more quickly. Let's take a look at what that mea... | [{"start": 0.0, "end": 7.8, "text": " I'd like to share with you some practical tips for developing an anomaly detection system."}, {"start": 7.8, "end": 13.84, "text": " One of the key ideas will be that if you can have a way to evaluate a system even as it's"}, {"start": 13.84, "end": 19.16, "text": " being developed... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=7dIVRduaHzQ | 8.12 Anomaly Detection | Anomaly detection vs. supervised learning -- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | When you have a few positive examples with y equals 1 and a large number of negative examples say y equals 0, when should you use anomaly detection and when should you use supervised learning? The decision is actually quite subtle in some applications, so let me share with you some thoughts and some suggestions for ho... | [{"start": 0.0, "end": 8.44, "text": " When you have a few positive examples with y equals 1 and a large number of negative"}, {"start": 8.44, "end": 13.86, "text": " examples say y equals 0, when should you use anomaly detection and when should you use"}, {"start": 13.86, "end": 19.5, "text": " supervised learning? Th... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=hBiFuWa91wE | 8.13 Anomaly Detection | Choosing what features to use -- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | When building an anomaly detection algorithm, I found that choosing a good choice of features turns out to be really important. In supervised learning, if you don't have the features quite right, or if you have a few extra features that are not relevant to the problem, that often turns out to be okay because the algor... | [{"start": 0.0, "end": 8.28, "text": " When building an anomaly detection algorithm, I found that choosing a good choice of features"}, {"start": 8.28, "end": 11.040000000000001, "text": " turns out to be really important."}, {"start": 11.040000000000001, "end": 15.08, "text": " In supervised learning, if you don't hav... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=1aKPXsx54Ms | 9.1 Recommender System | Making recommendations -- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Welcome to this second to last week of the machine learning specialization. I'm really happy that together we're almost all the way to the finish line. What we'll do this week is discuss recommender systems. This is one of the topics that has received quite a bit of attention in academia, but the commercial impact and... | [{"start": 0.0, "end": 6.8, "text": " Welcome to this second to last week of the machine learning specialization."}, {"start": 6.8, "end": 11.68, "text": " I'm really happy that together we're almost all the way to the finish line."}, {"start": 11.68, "end": 15.24, "text": " What we'll do this week is discuss recommend... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=udHt4CiJH6M | 9.2 Collaborative Filtering | Using per-item features-- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | So, let's take a look at how we can develop a recommender system if we had features of each item or features of each movie. So here's the same data set that we had previously with the four users having rated some but not all of the five movies. What if we additionally have features of the movies? So here I've added tw... | [{"start": 0.0, "end": 7.5200000000000005, "text": " So, let's take a look at how we can develop a recommender system if we had features of"}, {"start": 7.5200000000000005, "end": 10.72, "text": " each item or features of each movie."}, {"start": 10.72, "end": 16.22, "text": " So here's the same data set that we had pr... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=lrNPMtBH75w | 9.3 Collaborative Filtering | Collaborative filtering algorithm-- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In the last video, you saw how if you have features for each movie, such as features X1 and X2, they tell you how much is this a romance movie and how much is this an action movie. Then you could use basically linear regression to learn to predict movie ratings. But what if you don't have those features, X1 and X2? Le... | [{"start": 0.0, "end": 7.44, "text": " In the last video, you saw how if you have features for each movie, such as features"}, {"start": 7.44, "end": 12.88, "text": " X1 and X2, they tell you how much is this a romance movie and how much is this an action"}, {"start": 12.88, "end": 13.88, "text": " movie."}, {"start": ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=tnIiuLQk63I | 9.4 Collaborative Filtering | Binary labels: favs, likes and clicks-- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Many important applications of recommended systems or collaborative filtering algorithms involve binary labels where instead of a user giving you a 1 to 5 star or 0 to 5 star rating, they just somehow give you a sense of they like this item or they did not like this item. Let's take a look at how to generalize the alg... | [{"start": 0.0, "end": 8.22, "text": " Many important applications of recommended systems or collaborative filtering algorithms"}, {"start": 8.22, "end": 15.700000000000001, "text": " involve binary labels where instead of a user giving you a 1 to 5 star or 0 to 5 star rating,"}, {"start": 15.700000000000001, "end": 21... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=vF96LPtVM-A | 9.5 Recommender Systems implementation | Mean normalization-- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Back in the first course, you had seen how for linear regression, feature normalization can help the algorithm run faster. In the case of building and recommend a system with numbers y, such as movie ratings from 1 to 5 or 0 to 5 stars, it turns out your algorithm will run more efficiently and also perform a bit bette... | [{"start": 0.0, "end": 7.16, "text": " Back in the first course, you had seen how for linear regression, feature normalization"}, {"start": 7.16, "end": 9.9, "text": " can help the algorithm run faster."}, {"start": 9.9, "end": 15.96, "text": " In the case of building and recommend a system with numbers y, such as movi... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=VIecm37hBuA | 9.6 Recommender Systems implementation detail|TensorFlow implementation of collaborative filtering | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In this video, we'll take a look at how you can use TensorFlow to implement the collaborative filtering algorithm. You might be used to thinking of TensorFlow as a tool for building neural networks, and it is. It's a great tool for building neural networks. And it turns out that TensorFlow can also be very helpful for... | [{"start": 0.0, "end": 7.24, "text": " In this video, we'll take a look at how you can use TensorFlow to implement the collaborative"}, {"start": 7.24, "end": 9.44, "text": " filtering algorithm."}, {"start": 9.44, "end": 14.08, "text": " You might be used to thinking of TensorFlow as a tool for building neural network... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=uXMa7YwDVbE | 9.7 Collaborative Filtering | Finding related items-- [Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | If you go to an online shopping website and are looking at a specific item, say maybe a specific book, the website may show you things like, here are some other books similar to this one. Or if you're browsing a specific movie, it may say, here are some other movies similar to this one. How do the websites do that so ... | [{"start": 0.0, "end": 8.2, "text": " If you go to an online shopping website and are looking at a specific item, say maybe"}, {"start": 8.2, "end": 13.280000000000001, "text": " a specific book, the website may show you things like, here are some other books similar"}, {"start": 13.280000000000001, "end": 14.280000000... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=UgSBaD7s5HU | 9.8 Content-based Filtering | Collaborative filtering vs Content-based filtering-- [ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In this video, we'll start to develop a second type of recommender system called a content-based filtering algorithm. To get started, let's compare and contrast the collaborative filtering approach that we've been looking at so far with this new content-based filtering approach. Let's take a look. With collaborative f... | [{"start": 0.0, "end": 8.0, "text": " In this video, we'll start to develop a second type of recommender system called a content-based"}, {"start": 8.0, "end": 9.8, "text": " filtering algorithm."}, {"start": 9.8, "end": 13.8, "text": " To get started, let's compare and contrast the collaborative filtering approach tha... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=jhgnQB7fYKM | 9.9 Content-based Filtering | Deep learning for content-based filtering-- [ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | A good way to develop a content-based filtering algorithm is to use deep learning. The approach you see in this video is the way that many important commercial, state-of-the-art content-based filtering algorithms are built today. Let's take a look. Recall that in our approach, given a feature vector describing a user,... | [{"start": 0.0, "end": 6.8, "text": " A good way to develop a content-based filtering algorithm is to use deep learning."}, {"start": 6.8, "end": 13.56, "text": " The approach you see in this video is the way that many important commercial, state-of-the-art"}, {"start": 13.56, "end": 15.56, "text": " content-based filt... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=nNJPU5fwc8E | 9.10 Advanced implementation | Recommending from a large catalogue -- [ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Today's recommended systems will sometimes need to pick a handful of items to recommend from a catalog of thousands or millions or tens of millions or even more items. How do you do this efficiently computationally? Let's take a look. Here's the neural network we've been using to make predictions about how a user migh... | [{"start": 0.0, "end": 6.44, "text": " Today's recommended systems will sometimes need to pick a handful of items to recommend"}, {"start": 6.44, "end": 11.6, "text": " from a catalog of thousands or millions or tens of millions or even more items."}, {"start": 11.6, "end": 13.72, "text": " How do you do this efficient... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=GGnPWaNjaGM | 9.11 Advanced implementation | Ethical use of recommender systems -- [ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Even though recommender systems have been very profitable for some businesses, there have been some use cases that have left people and society at large worse off. However you use recommender systems, or for that matter other learning algorithms, I hope you only do things that make society at large and people better o... | [{"start": 0.0, "end": 6.8, "text": " Even though recommender systems have been very profitable for some businesses, there"}, {"start": 6.8, "end": 13.96, "text": " have been some use cases that have left people and society at large worse off."}, {"start": 13.96, "end": 19.2, "text": " However you use recommender syste... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=_ofRJ2tS_v8 | 9.12 Content-based Filtering| TensorFlow implementation of content-based filtering -[ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In the practice lab, you'll see how to implement content-based filtering in TensorFlow. What I'd like to do in this video is just step through with you a few of the key concepts in the code that you get to play with. Let's take a look. Recall that our code has started with a user network as well as a movie network. An... | [{"start": 0.0, "end": 7.28, "text": " In the practice lab, you'll see how to implement content-based filtering in TensorFlow."}, {"start": 7.28, "end": 11.94, "text": " What I'd like to do in this video is just step through with you a few of the key concepts"}, {"start": 11.94, "end": 14.42, "text": " in the code that... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=I-z8EreP1Bs | 10.1 Reinforcement Learning Introduction | What is Reinforcement Learning? -[ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Welcome to this final week of the machine learning specialization. It's a little bit bittersweet for me that we're approaching the end of this specialization, but I'm looking forward to this week sharing with you some exciting ideas about reinforcement learning. In machine learning, reinforcement learning is one of th... | [{"start": 0.0, "end": 6.0, "text": " Welcome to this final week of the machine learning specialization."}, {"start": 6.0, "end": 10.0, "text": " It's a little bit bittersweet for me that we're approaching the end of this specialization,"}, {"start": 10.0, "end": 15.0, "text": " but I'm looking forward to this week sha... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=vxmDWRTi0PM | 10.2 Reinforcement Learning formalism | Mars rover example -[ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | To flesh out the reinforcement learning formalism, instead of looking at something as complicated as a helicopter or a robot dog, we're going to use a simplified example that's loosely inspired by the Mars Rover. This is adapted from an example due to Stanford professor Emma Brunskell and one of my collaborators Jack ... | [{"start": 0.0, "end": 5.5600000000000005, "text": " To flesh out the reinforcement learning formalism,"}, {"start": 5.5600000000000005, "end": 10.8, "text": " instead of looking at something as complicated as a helicopter or a robot dog,"}, {"start": 10.8, "end": 17.240000000000002, "text": " we're going to use a simp... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=hdG6-fCsKh4 | 10.3 Reinforcement Learning formalism | The Return in reinforcement learning -[ML |Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | You saw in the last video what are the states of a reinforcement learning application, as well as how depending on the actions you take, you go through different states and also get to enjoy different rewards. But how do you know if a particular set of rewards is better or worse than a different set of rewards? The re... | [{"start": 0.0, "end": 6.76, "text": " You saw in the last video what are the states of a reinforcement learning application, as"}, {"start": 6.76, "end": 12.120000000000001, "text": " well as how depending on the actions you take, you go through different states and also get"}, {"start": 12.120000000000001, "end": 14.... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Zd9npj3C6QE | 10.4 Reinforcement Learning formalism | Making decisions: Policies in reinforcement learning -ML| Ng | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Let's formalize how a reinforcement learning algorithm picks actions. In this video, you'll learn about what is a policy in a reinforcement learning algorithm. Let's take a look. As we've seen, there are many different ways that you can take actions in a reinforcement learning problem. For example, we could decide to ... | [{"start": 0.0, "end": 6.12, "text": " Let's formalize how a reinforcement learning algorithm picks actions."}, {"start": 6.12, "end": 10.88, "text": " In this video, you'll learn about what is a policy in a reinforcement learning algorithm."}, {"start": 10.88, "end": 12.76, "text": " Let's take a look."}, {"start": 12... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=zRhXn9n05b4 | 10.5 Reinforcement Learning formalism | Review of key concepts -[Machine Learning| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | We've developed a reinforcement learning formalism using the six-state mass rover example. Let's do a quick review of the key concepts and also see how this set of concepts can be used for other applications as well. Some of the concepts we've discussed are states of a reinforcement learning problem, the set of action... | [{"start": 0.0, "end": 8.76, "text": " We've developed a reinforcement learning formalism using the six-state mass rover example."}, {"start": 8.76, "end": 13.44, "text": " Let's do a quick review of the key concepts and also see how this set of concepts can"}, {"start": 13.44, "end": 16.84, "text": " be used for other... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=dVmFCFiON3E | 10.6 State-action value function | State-action value function definition -[ML| Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | When we start to develop reinforcement learning arrows later this week, you see that there's a key quantity that reinforcement learning arrows will try to compute, and that's called the state action value function. Let's take a look at what this function is. The state action value function is a function typically deno... | [{"start": 0.0, "end": 5.72, "text": " When we start to develop reinforcement learning arrows later this week,"}, {"start": 5.72, "end": 8.34, "text": " you see that there's a key quantity that"}, {"start": 8.34, "end": 10.74, "text": " reinforcement learning arrows will try to compute,"}, {"start": 10.74, "end": 13.9,... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=kbIedKCb94I | 10.7 State-action value function | State-action value function example -[Machine Learning|Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Using the Mars Rover example, you've seen what the values of QSA are like. In order to keep holding our intuition about reinforcement learning problems and how the values of QSA change depending on the problem, we've provided an optional lab that lets you play around, modify the Mars Rover example and see for yourself... | [{"start": 0.0, "end": 4.2, "text": " Using the Mars Rover example,"}, {"start": 4.2, "end": 7.88, "text": " you've seen what the values of QSA are like."}, {"start": 7.88, "end": 11.24, "text": " In order to keep holding our intuition about"}, {"start": 11.24, "end": 14.48, "text": " reinforcement learning problems an... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=Jv8cKma_yIs | 10.8 State-action value function | Bellman Equations -[Machine Learning|Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Let me summarize where we are. If you can compute the state action value function q of s, a, then it gives you a way to pick a good action from every state. Just pick the action a that gives you the largest value of q of s, a. So the question is, how do you compute these values q of s, a? In reinforcement learning, th... | [{"start": 0.0, "end": 4.44, "text": " Let me summarize where we are."}, {"start": 4.44, "end": 8.4, "text": " If you can compute the state action value function q of s,"}, {"start": 8.4, "end": 13.0, "text": " a, then it gives you a way to pick a good action from every state."}, {"start": 13.0, "end": 17.0, "text": " ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=-5W5c1ZrSZ8 | 10.9 State-action value function | Random (stochastic) environment (Optional) -[ML | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In some applications, when you take an action, the outcome is not always completely reliable. For example, if you command your Mars rover to go left, maybe there's a little bit of a rock slide or maybe the floor is really slippery and so it slips and goes in the wrong direction. In practice, many robots don't always m... | [{"start": 0.0, "end": 9.040000000000001, "text": " In some applications, when you take an action, the outcome is not always completely reliable."}, {"start": 9.040000000000001, "end": 13.82, "text": " For example, if you command your Mars rover to go left, maybe there's a little bit of"}, {"start": 13.82, "end": 18.16... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=VjWHeGciHQE | 10.10 Continuous State Spaces | Example of continuous state space applications -[ML | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Many robotic control applications, including the lunar lander application that you work on in the practice lab, have continuous state spaces. Let's take a look at what that means and how to generalize the concepts we've talked about to these continuous state spaces. The simplified Mars rover example we use, had used a... | [{"start": 0.0, "end": 4.5600000000000005, "text": " Many robotic control applications,"}, {"start": 4.5600000000000005, "end": 9.16, "text": " including the lunar lander application that you work on in the practice lab,"}, {"start": 9.16, "end": 11.52, "text": " have continuous state spaces."}, {"start": 11.52, "end":... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=d8w5g94Wa9E | 10.11 Continuous State Spaces | Lunar lander -[Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | The lunar lander lets you land a simulated vehicle on the moon. It's like a fun little video game that's been used by a lot of reinforcement learning researchers. Let's take a look at what it is. In this application, you're in command of a lunar lander that is rapidly approaching the surface of the moon, and your job ... | [{"start": 0.0, "end": 6.88, "text": " The lunar lander lets you land a simulated vehicle on the moon."}, {"start": 6.88, "end": 12.200000000000001, "text": " It's like a fun little video game that's been used by a lot of reinforcement learning researchers."}, {"start": 12.200000000000001, "end": 14.76, "text": " Let's... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=02vHFyFzhqw | 10.12 Continuous State Spaces | Learning the state-value function -[Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Let's see how we can use reinforcement learning to control the lunar lander or for other reinforcement learning problems. The key idea is that we're going to train a neural network to compute or to approximate the state action value function Q of s, a, and that in turn will let us pick good actions. Let's see how this... | [{"start": 0.0, "end": 8.64, "text": " Let's see how we can use reinforcement learning to control the lunar lander or for other reinforcement"}, {"start": 8.64, "end": 10.200000000000001, "text": " learning problems."}, {"start": 10.200000000000001, "end": 16.86, "text": " The key idea is that we're going to train a ne... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=4hlH4TXtNms | 10.13 Continuous State Spaces|Algorithm refinement Improved neural network architecture-ML Andrew Ng | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In the last video, we saw a neural network architecture that would input the state in action and attempt to output the Q function, Q of sA. It turns out that there's a change to neural network architecture that makes this algorithm much more efficient. So most implementations of DQN actually use this more efficient ar... | [{"start": 0.0, "end": 7.640000000000001, "text": " In the last video, we saw a neural network architecture that would input the state in"}, {"start": 7.640000000000001, "end": 12.82, "text": " action and attempt to output the Q function, Q of sA."}, {"start": 12.82, "end": 17.12, "text": " It turns out that there's a ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=tX7L_441Jlo | 10.14 Continuous State Spaces | Algorithm refinement ϵ greedy policy -[Machine Learning | Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In the learning algorithm that we developed, even while you're still learning how to approximate QFSA, you need to take some actions in the lunar lander. So how do you pick those actions while you're still learning? The most common way to do so is to use something called an epsilon greedy policy. Let's take a look at ... | [{"start": 0.0, "end": 8.32, "text": " In the learning algorithm that we developed, even while you're still learning how to approximate"}, {"start": 8.32, "end": 13.88, "text": " QFSA, you need to take some actions in the lunar lander."}, {"start": 13.88, "end": 17.080000000000002, "text": " So how do you pick those ac... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=3FkPgerAhXo | 10.15 Continuous State Spaces | Algorithm refinement Mini-batch and soft updates (optional)-[ML-Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | In this video, we'll look at two further refinements to the reinforcement learning algorithm you've seen. The first idea is called using mini-batches, and this turns out to be an idea that can both speed up your reinforcement learning algorithm and is also applicable to supervised learning and can help you speed up yo... | [{"start": 0.0, "end": 7.44, "text": " In this video, we'll look at two further refinements to the reinforcement learning algorithm you've"}, {"start": 7.44, "end": 8.44, "text": " seen."}, {"start": 8.44, "end": 13.92, "text": " The first idea is called using mini-batches, and this turns out to be an idea that can"}, ... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=pdeGAhJ5pbE | 10.16 Continuous State Spaces |The state of reinforcement learning -[Machine Learning-Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Reinforcement learning is an exciting set of technologies. In fact, when I was working on my PhD thesis, reinforcement learning was the subject of my thesis. So I was and still am excited about these ideas. Despite all the research momentum and excitement behind reinforcement learning though, I think there is a bit or... | [{"start": 0.0, "end": 6.0, "text": " Reinforcement learning is an exciting set of technologies."}, {"start": 6.0, "end": 9.0, "text": " In fact, when I was working on my PhD thesis,"}, {"start": 9.0, "end": 12.0, "text": " reinforcement learning was the subject of my thesis."}, {"start": 12.0, "end": 16.0, "text": " S... |
Machine Learning Specialization 2022 -- Andrew Ng, Stanford University. | https://www.youtube.com/watch?v=GF1oHP5uDR8 | 10.17 Conclusion | Summary and Thank you --[Machine Learning-Andrew Ng] | Third and final Course:
Unsupervised Learning, Recommenders, Reinforcement Learning.
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Take heart! | Welcome to the final video of this machine learning specialization. We've been through a lot of videos together and this is the last one. Let's summarize the main topics we've gone over and then I'd like to say a few words and then we'll wrap up the class. Looking back, I think we've been through a lot together. The f... | [{"start": 0.0, "end": 6.32, "text": " Welcome to the final video of this machine learning specialization."}, {"start": 6.32, "end": 10.28, "text": " We've been through a lot of videos together and this is the last one."}, {"start": 10.28, "end": 15.0, "text": " Let's summarize the main topics we've gone over and then ... |
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