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| OK so finally let's get down to it and let's get into neural networks and I'll show you this is probably | |
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| the best explanation you'll ever see on your books online. | |
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| So let's get to it so fiercely why is this a best explanation. | |
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| I start off at a very high level explanation first. | |
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| I don't use much math. | |
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| And when I do it introduce it slowly and gradually I understand until you make you understand what makes | |
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| your life work so important. | |
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| Break down key elements in neural networks would back propagation which is something it's actually rare | |
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| to find. | |
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| And when you do find that online it is bit tricky to understand and I walked you really into descent | |
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| last functions activation functions all of which are very important to neural networks and I make you | |
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| understand what is actually going on in the training process something I rarely see taught in any tutorial | |
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| so fiercely What are neural networks neural networks technically because it's just a big bunch of weights | |
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| which are just matrices with numbers tend to be like a black box and we don't actually know how they | |
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| work. | |
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| Now that's kind of a lie. | |
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| We do know how they work conceptually. | |
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| We just it is so much information sometimes stored in a neural network. | |
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| We doing actually know what it knows. | |
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| Sometimes but essentially it takes inputs and predicts an output just like any other machine learning | |
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| algorithm. | |
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| However it's different and better than most traditional algorithms mainly because it can learn complex | |
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| non-linear and I'll describe what non-linear is soon mappings to former and produced far more accurate | |
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| model. | |
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| So you get better results from neural networks by far. | |
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| So this isn't mysterious but books will give it a picture of a cat and a nose as a cat how Same with | |
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| a gorilla. | |
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| Same with a dog. | |
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| So let's actually see what neuroethics actually do. | |
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| So this is a very very simplistic model of a neural network. | |
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| It has two inputs and these inputs are basically like pixels in an image. | |
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| You can consider them to be. | |
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| These are the hidden nodes. | |
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| These are basically what actually is a black box information. | |
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| The hidden area we don't actually know what these values are. | |
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| I mean we do know what is not is. | |
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| But there are so many of them we actually don't know how to actually interpret them as human. | |
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| And basically it does all these calculations in here and then upwards to categories. | |
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| This can be cat or dog. | |
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| So as I said this is basically the input layer here the hidden layer and output layer. | |
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| So in a very high level example we can have an input layer where we're giving the neural nets cut off | |
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| a blitz Fedder the weight of the bid and tells you the species and the sex of Tibet. | |
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| I mean that would be pretty cool. | |
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| I don't think it exists but it would be pretty cool. | |
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| So how exactly do we get a prediction. | |
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| We passed the inputs into a neural net and received an output. | |
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| So this is an example of how we actually how we actually can pass data. | |
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| Let's say it's a dark brown with white head. | |
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| That's a fair description DeWitt's and we get what it actually is a female. | |
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| So here's another example of neural nets that does handwritten The declassification. | |
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| So let's move onto it. | |
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| This is the original input right here and we have some hidden layers that we will talk about very soon. | |
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| So basically we feed this image input here and we get an output classification it being 4. | |
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| So this is an example of a neural net. | |
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| We're going to learn a lot more later on this. | |
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| This is at a high level it's conceptually how neural networks work. | |
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| OK. | |