So, neural networks, you may have heard a
bit about them and based on the fact that
you clicked on this video, you are probably
interested in them.
In this video, my goal is to introduce the
basic functions of a neural net, and in subsequent
videos I will go into detail, explaining how
exactly neural networks do what they do.
Before we begin, I just want to point out
that what I am currently referring to is artificial
neural networks, that is, software that emulates
what a real, human neural network would do.
For simplicity, in this video, and in the
following videos I will just be referring
to them as neural networks or even just neural
nets.
With that out of the way let’s dive into
the world of AI, or Artificial intelligence,
and neural networks.
The term neural network describes a subcategory
of AI.
AI itself is defined as a branch of computer
science dealing with the simulation of intelligent
behavior in computers.
In other words, AI attempts to make computers
imitate human intelligence.
Neural networks are a specific way of doing
this that involves the modeling of the human
brain itself and the nervous system.
It has recently been extremely successful.
The following are some of its applications:
Handwriting recognition, image compression,
stock exchange predictions, and even image
recognition, or recognizing what is in an
image, such as a labrador retriever, a cargo
ship, sneakers.
The basic idea of a neural network is that
you can take some input data, or usually many
inputs and perform some operations to them,
and based on this you get an output, a number
that can mean many things based on the context.
Of course, you first need to find the right
operations, in a process called training.
The aim of this video is not to explain how
this works, so I will leave it at that for
now.
As I said before, this will be the aim of
the following videos.
Now I will present a much simpler example,
and explain how the neural net could be used
to figure out the solution.
For example, you may want to classify flowers
into one of two species based on the lengths
and widths of its petals.
In this case, you would have two inputs (the
length and width of that flower’s pedal),
and one output (the classification of the
flower, or basically what species it is).
Keep in mind that inputs would be numbers,
and as a result, the output will be as well,
you could say that any flower with an output
above 0.5 would be one species and anything
below 0.5 is another.
As I have previously said, before the neural
network can successfully classify the flowers,
you will need to adjust the operations that
are performed, in the training step.
In this case, this would involve using raw
data--the lengths and widths of pedals and
species of a particular flower, for many flowers--to
adjust the operations, making the neural network
capable of classification.
How exactly we do this will be one of the
main topics of subsequent videos.
Basically, training will require inputs and
outputs from real data, in order for the neural
network to create accurate predictions of
the output when new inputs are presented.
Now that you understand some of the applications
and uses of neural networks, and a basic idea
of how they work, let’s continue our journey,
exploring the ins and outs of neural networks.
In our next video where we discuss the basic
structure of a neural network.
In the video following this, we will take
a look at how we can make our neural network
capable of training.
I’ll see you then.
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