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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.