    Gain insights into building and optimizing neural networks in Python. Delve into fundamental concepts, mathematical explanations, and practical implementations to enhance your machine learning skills.

Make your own neutral network in python final.png

Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to teach one of the fundamental concepts of machine learning, i.e., Neural Network. You will learn the basic concepts of building a model as well as the mathematical explanation behind Neural Network and based on that; you will build one from scratch (in Python). You will also learn how to train and optimize your network to achieve a better result.

Make Your Own Neural Network in Python

## Data preprocessing 
We've learned how to get data out of the MNIST data files and disentangle it so we can make sense of it and visualize it. We want to train our neural network with this data, but we need to think about preparing this data before we throw it at our neural network.

We saw earlier that neural networks work better if the input data and the output values are of the right shape, so that they stay within the comfort zone of the network node activation functions.

## Rescaling 
First we need to rescale the input color values from the larger range $0–255$ to the much smaller range $0.01–1.0$. We've deliberately chosen $0.01$ as the lower end of the range to avoid the problems we saw earlier with zero valued inputs, because they can artificially kill weight updates. We don't have to choose $0.99$ for the upper end of the input, because we don't need to avoid $1.0$ for the inputs. It's only for the outputs that we should avoid the impossible to reach $1.0$.

Dividing the raw inputs, which are in the range $0–255$ by $255$, will bring them into the range $0-1$. Then we need to multiply by $0.99$ to bring them into the range $0.0–0.99$. Next we add $0.01$ to shift them up to the desired range $0.01–1.00$. The following Python code shows this in action:

# Data preprocessing 
We've learned how to get data out of the MNIST data files and disentangle it so we can make sense of it and visualize it. We want to train our neural network with this data, but we need to think about preparing this data before we throw it at our neural network.

We saw earlier that neural networks work better if the input data and the output values are of the right shape, so that they stay within the comfort zone of the network node activation functions.

# Rescaling 
First we need to rescale the input color values from the larger range $0–255$ to the much smaller range $0.01–1.0$. We've deliberately chosen $0.01$ as the lower end of the range to avoid the problems we saw earlier with zero valued inputs, because they can artificially kill weight updates. We don't have to choose $0.99$ for the upper end of the input, because we don't need to avoid $1.0$ for the inputs. It's only for the outputs that we should avoid the impossible to reach $1.0$.

Dividing the raw inputs, which are in the range $0–255$ by $255$, will bring them into the range $0-1$. Then we need to multiply by $0.99$ to bring them into the range $0.0–0.99$. Next we add $0.01$ to shift them up to the desired range $0.01–1.00$. The following Python code shows this in action:

Learn how to rescale the input values to a smaller range, 0–1, to prepare it for training. 

Prologue

A Little Background

Let's Get Started!

Backward Propagation of Error

Adjusting the Link Weights

A Gentle Start with Python

Neural Network with Python

Testing Neural Network against MNIST Dataset

Some Suggested Improvements

Even More Fun!

Epilogue

Appendix: A Small Guide to Calculus

Prepare the MNIST Training Data

Data preprocessing

Rescaling