    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

## The difference between a classifier and a predictor
The model we built in the previous lesson is called a **predictor** because it takes an input and makes a prediction about what the output should be. We refined that prediction by adjusting an internal parameter, informed by the errors we saw when comparing the answers with a true example. Now, let's look at the following graph, showing the widths and lengths of garden bugs.

# The difference between a classifier and a predictor
The model we built in the previous lesson is called a **predictor** because it takes an input and makes a prediction about what the output should be. We refined that prediction by adjusting an internal parameter, informed by the errors we saw when comparing the answers with a true example. Now, let's look at the following graph, showing the widths and lengths of garden bugs.

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

Classify vs. Predict

The difference between a classifier and a predictor