    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

## Simple activation function
A function that takes the input signal and generates an output signal, but takes into account some kind of threshold, is called an **activation function**.
 
Mathematically speaking, there are many activation functions that could achieve this effect. A simple step function could do this.

# Simple activation function
A function that takes the input signal and generates an output signal, but takes into account some kind of threshold, is called an **activation function**.
 
Mathematically speaking, there are many activation functions that could achieve this effect. A simple step function could do this.

Learn about different activation functions and how to apply them to neurons.

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

Activation Functions

Simple activation function