    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.

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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

## An example of calculating a weight update 
The following network is one we've worked with before, but this time we’ve added example output values from the first hidden node $o_{j=1}$ and the second hidden node $o_{j=2}$. These are just made-up numbers to illustrate the method and aren’t properly worked out by feeding signals forward from the input layer.


# An example of calculating a weight update 
The following network is one we've worked with before, but this time we’ve added example output values from the first hidden node $o_{j=1}$ and the second hidden node $o_{j=2}$. These are just made-up numbers to illustrate the method and aren’t properly worked out by feeding signals forward from the input layer.


Understand the weight update method in a neural network with the help of a numerical example.

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

Weight Updates Calculated

An example of calculating a weight update