    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

##  Recombination of errors
The training data examples only tell us what the outputs from the final nodes should be. They don’t tell us what the outputs from nodes in any other layer should be. This is the core of the puzzle. 

We could recombine the split errors for the links using the error backpropagation we just saw earlier.

# Recombination of errors
The training data examples only tell us what the outputs from the final nodes should be. They don’t tell us what the outputs from nodes in any other layer should be. This is the core of the puzzle. 

We could recombine the split errors for the links using the error backpropagation we just saw earlier.

Learn how to recombine split errors for the links using error backpropagation.

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

Backpropagation: Recombine the Error

Recombination of errors