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Now we are going to begin our journey into the field of neural networks, work on neural networks,

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began with the motivation that human brain computes in a very different way than a conventional computer.

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And human brain is able to perform certain types of computations, such as image recognition and pattern

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recognition, much better than a conventional computer.

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On the other hand, computers are very good at complex arithmetic calculations, which human brain is

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not very good at.

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Let's take an example of recognizing handed Inderjit.

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Recognizing so many different types of handwritten digits is not easy, but we humans are astoundingly

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good at making sense of these digits.

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On the other hand, if we have to write a computer program to the recognize did it like these.

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Can you imagine how difficult it is?

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What will be the tool to see that these two are the same digit nine and this one is a seven and not

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a nine?

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When we try to make such precise rules, we get stuck with a number of exceptions, caveats and special

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cases.

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This is where neural networks come in.

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Neural networks do not have the inbuilt rules.

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Rather, neural networks infer these rules for recognizing handwriting from a given large set of handwritten

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digits, which is also known as the training examples.

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So basically we will take a large set of data feed into the neural network.

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Neural network will make some rules and we will use this brain neural network to predict for the other

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cases.

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This ability to create own rules makes neural network versatile, powerful and scalable.

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And this course, we are going to take the example of fashion m NASD.

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Fashion m NASD is a data set in which we classify images into 10 different fashion items in this dataset.

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We will have images of several fashion items and we will classify them into 10 different categories.

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For example, the first image is of her ankle boot.

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The second is a pullover.

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Third is a trouser and so on.

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So we will have grayscale images of several fashion items and will classify them into several fashion

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product.

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This problem statement is going to be significantly more challenging than the handwriting recognition

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example.

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And once you are done with this, you will appreciate the portal neural networks.

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Let me give you an overview of what the next few lectures are going to be.

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We will start the conceptual part by understanding two very important artificial neural cells called

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Perceptron and a sigmoid neuron.

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Then we will stack these single cells into multiple cells and we will make a multi-level Perceptron.

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A multi-level Perceptron is the most commonly used neural network model.

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Then we will dig deeper and understand forward and backward propagation.

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Then we move on to stochastic gradient descent, which is the standard learning algorithm for neural

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networks and protocols.

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We will be implementing all these models in Python.

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And this course, I will provide you with the reason of why we are doing whatever we are doing.

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So by the end of the course, you will understand what neural network and deep learning is, how to

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create a neural network to solve complex problems, and when and where to use neural networks.