In this section of the course we are going to introduce the idea of artificial known that works or eons
for short.
Specifically we're going to talk about a certain kind of artificial known that work called a feed forward
neural network.
This is the most basic kind of neuron that work but as you'll see the concepts involved go quite deep
and they also form the basis for other kinds of neural networks such as convolution on neuron that works
and recurrent or networks
to begin this discussion.
Let's start with why and how neural networks came to be in the first place.
This is something that a lot of teachers skip over but it's something that I find quite interesting
and inspirational.
If you think Neural Networks are a cool new model to help you pick stocks or play Mario Kart then you
are still looking for the small fish.
In fact at networks are way more interesting than that
as you may have realized that the name artificial known that work means that we are trying to artificially
create a neural network in a computer.
OK so then what's a real name on that work as in a non artificial neural network the name neuron that
network derives from neurons which are the cells in your brain and extend throughout your nervous system.
Now this is probably too obvious for most of you but let's state it anyway just in case your brain is
what you used to think.
Neurons in your brain are connected to each other and can communicate with each other via electrical
and chemical signals.
Amazingly this simple physical and chemical system is what makes you you.
All of your thoughts and aspirations your emotions and every action you take throughout the day.
It's all driven by your neurons which in turn are just sending electrical and chemical signals around
amongst themselves
once scientists realize what the brain was doing and what it was responsible for.
The next question was almost obvious in hindsight can we build a brain.
I mean if the brain is just a network of neurons and we can simulate neurons in a computer.
If we connect a bunch of neurons through a computer simulation it seems that it might be possible to
create some form of intelligence and artificial intelligence.
You might say
so let's take our model of a single neuron logistic regression.
Now let's imagine that we have multiple neurons all taking in the same inputs but computing something
different now.
Let's do it again.
Now we have multiple logistic regressions.
Now let's imagine that all these neurons are connected to more neurons.
So we just repeat the process pretending that the new layer of neurons are actually inputs to more neurons.
That's basically a very tiny model of the brain neurons connected to neurons.
Of course this is necessarily simplistic.
One side is the input and one side is the output.
Of course the actual brain is much more complex.
There are many inputs and many outputs in the middle.
Wires can crisscross if we have a later neuron connecting back to an earlier neuron.
We call that a recurrent connection.
The neural networks we are about to discuss in this section contained no such complexities.
Instead because the input is on one side and the output is on the other side and we go from input to
output in a layer wise fashion we call this a feed forward neural network
in the rest of this lecture.
We are going to outline what we will discuss in this section of the course.
First we are going to start out again by discussing the model architecture.
As you know the model we'll be discussing in this section is the feed forward neuron that we're the
next step after discussing the model architecture will be to go back to the geometric picture.
If you recall my model goes machine learning is nothing but a geometry problem.
So how do neural networks extend the capabilities of a basic linear model in terms of solving this geometry
problem.
Next we're going to go more in-depth and discuss activation functions activation functions are very
important in neural networks.
They are what make a neural networks more expressive than the simple linear models you saw in the previous
section.
After that we're going to discuss how to do multi class classification using neural networks.
If you recall in the previous section we only discussed the binary classification.
This works if we only have two classes dog or cat fraud or no fraud purchase or leave the store and
so on.
But what if we have more classes for example we might be working on a self-driving car that needs to
be able to recognize multiple kinds of objects on the road.
In this case a binary classification is not good enough and we need multi class classification.
Next we'll switch gears from talking about the known that work itself to a new kind of data images Deep
Learning excels on unstructured data such as images text and sound.
We'll look at how images are represented in a computer so that we can get back to the situation where
we can say all data is the same.
Finally we'll look at some notebooks that demonstrate how to do image classification using a neural
network for multiple classes.
We'll also look at noon that works for regression and see how they can be much more expressive than
a simple linear model.