Original subtitles

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Hello and welcome to the intuition tutorials for the artificial neural networks part of the course.

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Super excited to get these things started.

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And today we're going to find out how we're going to tackle this section.

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So in this section we will learn the following things.

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First of all we'll talk about the neuron so there'll be a little bit of neuroscience and we'll find

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out a bit about how the human brain works and why we are trying to replicate that.

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And we'll also see what the main building block of a neural network of the neuron looks like.

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Then in the next tutorial We'll talk about the activation function and we'll look at a couple of examples

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of attrition functions that you could use in your neural networks and we'll find out which ones of which

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one of them is the most commonly used one in neural networks and in which layers you'd rather use which

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functions.

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There was talk about how neural networks work.

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So in contrast to what you would expect and what it was probably conveyed in other courses and tutorials

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we're not going to go into the learning we're actually going to go into the working of the neural networks

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first because that way by seeing a neural network in action that will allow us to understand what we're

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aiming towards what our goal is.

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So here we'll look at an example of a neural network we're going to look at a very simplified and very

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simplified hypothetical example or when your own network working to predict housing prices are basically

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real estate prices.

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And by looking at that example we'll understand better exactly what we're aiming towards and what we

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want to achieve in the end.

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And then we will move on to understanding how neural networks learn because that way we'll be more prepared

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for what's coming.

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Then we'll talk about gradient descent.

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This is also part of neural networks learning and we'll understand how that algorithm is better than

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just the brute force method that you might be intending or willing to take as a first resort or first

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method that comes to mind.

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So we'll find out how great the advantage of gradient descent are.

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And then we'll talk about stochastic gradient descent.

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It's a it's a continuation of the great and decent tutorial but it's an even better and even stronger

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method and we'll find out exactly how it works.

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And finally we'll wrap things up by mentioning the important things about back propagation and summarizing

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everything in a step by step set of instructions for running your artificial neural networks.

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I hope this all sounds very exciting to you because I am very excited myself and I can't wait to get

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started.

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I look forward to seeing you on the first tutorial.

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And until then enjoy deep learning.