    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

## Calculate the constant $c$

What should we do to work out that missing constant $c$? Let’s pick a value at random and give it a go! Let's try $c = 0.5$ and see what happens. 

# Calculate the constant $c$

What should we do to work out that missing constant $c$? Let’s pick a value at random and give it a go! Let's try $c = 0.5$ and see what happens. 

Learn to use the hit-and-miss technique to estimate a constant and refine the model by adjusting other parameters based on the actual output.

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

Estimate the Constant Iteratively

Calculate the constant $c$