Now we are going to begin our journey into the field of neural networks, work on neural networks,
began with the motivation that human brain computes in a very different way than a conventional computer.
And human brain is able to perform certain types of computations, such as image recognition and pattern
recognition, much better than a conventional computer.
On the other hand, computers are very good at complex arithmetic calculations, which human brain is
not very good at.
Let's take an example of recognizing handed Inderjit.
Recognizing so many different types of handwritten digits is not easy, but we humans are astoundingly
good at making sense of these digits.
On the other hand, if we have to write a computer program to the recognize did it like these.
Can you imagine how difficult it is?
What will be the tool to see that these two are the same digit nine and this one is a seven and not
a nine?
When we try to make such precise rules, we get stuck with a number of exceptions, caveats and special
cases.
This is where neural networks come in.
Neural networks do not have the inbuilt rules.
Rather, neural networks infer these rules for recognizing handwriting from a given large set of handwritten
digits, which is also known as the training examples.
So basically we will take a large set of data feed into the neural network.
Neural network will make some rules and we will use this brain neural network to predict for the other
cases.
This ability to create own rules makes neural network versatile, powerful and scalable.
And this course, we are going to take the example of fashion m NASD.
Fashion m NASD is a data set in which we classify images into 10 different fashion items in this dataset.
We will have images of several fashion items and we will classify them into 10 different categories.
For example, the first image is of her ankle boot.
The second is a pullover.
Third is a trouser and so on.
So we will have grayscale images of several fashion items and will classify them into several fashion
product.
This problem statement is going to be significantly more challenging than the handwriting recognition
example.
And once you are done with this, you will appreciate the portal neural networks.
Let me give you an overview of what the next few lectures are going to be.
We will start the conceptual part by understanding two very important artificial neural cells called
Perceptron and a sigmoid neuron.
Then we will stack these single cells into multiple cells and we will make a multi-level Perceptron.
A multi-level Perceptron is the most commonly used neural network model.
Then we will dig deeper and understand forward and backward propagation.
Then we move on to stochastic gradient descent, which is the standard learning algorithm for neural
networks and protocols.
We will be implementing all these models in Python.
And this course, I will provide you with the reason of why we are doing whatever we are doing.
So by the end of the course, you will understand what neural network and deep learning is, how to
create a neural network to solve complex problems, and when and where to use neural networks.