‫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 us take an example of recognizing handed and digit 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 decode did it like these can you imagine
‫how difficult it is.
‫What will be the.
‫To say 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 caveat 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.
‫In this course we are going to take the example of fashion M NASD
‫fashion M NIST is a data set in which we classify images into N 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 offer ankle boot.
‫The second is a pullover.
‫It 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 power of 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
‫perception and a sigmoid neuron.
‫Then we will tagged these single cells into multiple cells and we will make a multi-level perception
‫on a multilevel perception 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 Mueller
‫networks and throughout the course we will be implementing all these models in Python.
‫In 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.