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And welcome to Chapter 15 points one way I explained to you what exactly is transfer learning and a

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fine tuning.

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So as we know from before trining complicated and deep CNN is very slow.

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Alex net and Visagie and partic. a deep parameter Laden networks Viji also and has 128 million parameters

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and resident 50 has 50 hidden layers despite being having less parameters.

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Still a lot of layers take some time to train.

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So at least that works a teen at relatively excellent performance on image at training them on you is

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definitely not recommended.

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You are not going to get anywhere close to good results.

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Even training for a moment when the C.P you Sidis CNN's are often trained for a couple of weeks or more

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using an array is of deep use.

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That's to tell you how complicated and how long it takes to get good results on image net.

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So what if there was a way we could reuse those pre-trained models and make it one classify as we've

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seen Scarus actually ships with Pretorian models on image net and those models with the models we showed

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you before in chapter 14.

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And these words are already tuned to detect dozens of low mid and high level features.

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What if we can use these that is already Treen that works now to below and classify as.

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And well we can introducing transfer learning and fine tuning this solves the problem.

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We just explained.

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So let's talk a bit about transitioning and fine tuning now.

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So fine tuning the concept of fine tuning is often and justifiably confused with transfer meaning.

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However it merely is a type of translating.

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Fine tuning is where we take a pre-trained deep CNN.

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So one of those like resonant or Viji as we've seen before.

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And we used the already trained typically on image net model to aid you image transformation tests typically

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in fine tuning We are taking an already trained CNN and turning it on to a new data set.

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OK.

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So basically what I'm seeing which I'll explain shortly is

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start of

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so let's take a look at these concepts.

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Firstly let's talk about fine tuning not the concept of fine tuning as often and very just by justifiably

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confused with transfer learning and that's because it's very similar and is merely a type of translating.

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Fine tuning is where we take a pre-trained deep CNN and we use this model.

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It's already been trained most likely on image net to basically asystole it.

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When you image classification tests.

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And typically in fine tuning We are taking an already trained CNN and turning it on when you a sense.

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So what we do is we then freeze the lower layers of this model and I'll illustrate to you what this

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means shortly.

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And we train only the top or fully connected layers.

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And that's how we actually train it.

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And you model here.

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So effectively we're just replacing the class or parts of an train model.

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And sometimes you can actually go back and unfreeze little weights and train them again to get even

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better performance.

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So let me explain this to you in the strictly what's happening.

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So imagine this is a deep much deeper than this.

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But imagine this is a deep CNN that's already been trained.

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All right.

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So when I say we freeze the layers we're freezing all the convolutional layers from the heat between

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the input and up to fully connectedly here.

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So these we imagine these we have already been trained and they're very good at picking up high low

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and mid-level features.

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So what we do know is we just basically change the classes that we want for our model and we basically

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just manipulate the top layer and Trinite on our data set now.

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So this is this is the first impulse here and this is the part we have basically modified and unfroze

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and are going to train the spot separately.

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So in fine tuning in most of CNN's THE FIRST FEW convolutional has long been low level features as explained.

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Those are like things like edge of textures of color blobs and that kind of stuff and so on.

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And as we progressed through a network it lends more high and mid-level features.

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So in fine tuning we just keep to the low levels frozen and we can also just treat the high level features

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as well so there's little steps here pretty much just went through this for you.

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But basically we freeze layers and we add or modify in a fully committed layer and we use a very tiny

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linning rate and we just initiate training again.

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It's quite easy to do in carrousel get to that code shortly and it's quite powerful.

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We by using these already well-trained models we can get superbly good accuracy on new image to us.

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So what about transitioning now as you've seen in finding we have taken an already Pretorian network

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and trained it or segments of it on some new data for a new image the best fit classification tests.

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All right.

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So translating is pretty much almost the same thing.

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And a lot of researchers and a lot of people in the industry use these terms interchangeably.

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However what transfinite linning really implies is that we're taking the knowledge from a Pretorian

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network and basically applying it to a similar tasks and therefore not really retreating much of the

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network.

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So what that means effectively is that let's go back to attack the in fine tuning.

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The reason why we call it fine tuning for is that where we can actually train these lawyers here.

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So it would transfer learning and in functioning we're basically unfreezing the top layer here and modifying

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it for our classes.

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But in fine tuning we can tend to go back and try the sleep here.

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That's to that's pretty much a core difference.

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So here's a quick quote from a deep learning book.

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I'm pretty sure you can click this link on the PDA slide that I give you basically a chance for cleaning

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and demean adaptation referred to a situation where what has already been learned or what has been learned

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in one setting is now exploited to improve generalization in another setting.

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That's effectively what transfer learning means.

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And we're going to do some practical examples now.

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We're going to use mobile net to create a monkey beach ossify.

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And then we're going to use Viji to create a flow or classify.

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So stay tuned and we are going to have some fun with these models.

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I guarantee.