    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

## Basic concepts 
Imagine we're in a car cruising at a constant 30 miles per hour (mph). Imagine we press the accelerator pedal. If we keep it pressed down, the speed of the car increases from 35, 40, 50, to 60 mph.

In this section, we'll explore the idea of things changing, like the speed of a car, and how to work out that change mathematically. What do we mean by mathematically? We mean understanding how things are related to each other so we can work out precisely how changes in one result in changes in another. This is called **calculus**.

By the end of this appendix, we’ll see that working out how things change in a mathematically precise way—because that’s all calculus really is—is not that difficult for many useful scenarios.

Even if we've done calculus or differentiation already, it's worth going through this section because we'll understand how calculus was invented. The ideas and tools used by those pioneering mathematicians can be very helpful when trying to solve different  problems in the future.

If you enjoy a good historic rivalry, the drama between Leibniz and Newton, who both claimed to have invented calculus first, can be entertaining.

# Basic concepts 
Imagine we're in a car cruising at a constant 30 miles per hour (mph). Imagine we press the accelerator pedal. If we keep it pressed down, the speed of the car increases from 35, 40, 50, to 60 mph.

In this section, we'll explore the idea of things changing, like the speed of a car, and how to work out that change mathematically. What do we mean by mathematically? We mean understanding how things are related to each other so we can work out precisely how changes in one result in changes in another. This is called **calculus**.

By the end of this appendix, we’ll see that working out how things change in a mathematically precise way—because that’s all calculus really is—is not that difficult for many useful scenarios.

Even if we've done calculus or differentiation already, it's worth going through this section because we'll understand how calculus was invented. The ideas and tools used by those pioneering mathematicians can be very helpful when trying to solve different  problems in the future.

If you enjoy a good historic rivalry, the drama between Leibniz and Newton, who both claimed to have invented calculus first, can be entertaining.

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

A Gentle Introduction to Calculus

Basic concepts