-: Hello, and welcome to the intuition tutorials
for the Artificial Neural Networks part of the course.
Super excited to get these things started
and today we're going to find out
how we're going to tackle this section.
So in this section, we will learn the following things.
First of all, we'll talk about the neuron.
So, there'll be a little bit of a neuroscience
and we'll find out a bit about how the human brain works
and why we are trying to replicate that.
And we'll also see what the main building block
of a neural network of the neuron looks like.
Then in the next tutorial we'll talk about
the activation function,
and we'll look at a couple of examples
of activation functions that you could use
in your neural networks,
and we'll find out which one of them
is the most commonly used one in neural networks
and in which layers you would rather use which functions.
Then we'll talk about how neural networks work.
So, in contrast to what you would expect
and what it was probably conveyed
in other courses and tutorials,
we're not going to go into the learning,
we're actually going to go into the working
of the neural networks first
because that way, by seeing a neural network in action,
that will allow us to understand what we're aiming towards,
what our goal is.
So here we'll look at an example of a neural network.
We're going to look at a very simplified,
a very simplified hypothetical example
of a neural network working,
to predict housing prices.
So basically, real estate prices.
And by looking at that example, we'll understand better
exactly what we're aiming towards
and what we want to achieve in the end.
And then we will move on to understanding
how neural networks learn
because that way we'll be more prepared for what's coming.
Then we'll talk about gradient descent.
This is also part of neural networks learning
and we'll understand how that algorithm
is better than just the brute force method
that you might be intending,
or willing to take as a first resort,
or first method that comes to mind.
So, we'll find out how,
what the advantage of gradient descent are.
And then we'll talk about stochastic gradient descent.
It's a continuation of the gradient descent tutorial,
but it's an even better and even stronger method
and we'll find out exactly how it works.
And finally, we'll wrap things up
by mentioning the important things about back propagation
and summarizing everything in a step by step
set of instructions
for running your artificial neural networks.
I hope this all sounds very exciting to you
because I am very excited myself
and I can't wait to get started.
I look forward to seeing you
on the first tutorial, and until then,
enjoy deep learning.