    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

## Create the basic structure of a neural network
We know we need to set the number of input, hidden, and output layer nodes. This defines the shape and size of the neural network. We'll use parameters to set their values. This way, we retain the choice to create new neural networks of different sizes with ease.

There's an important point here. Good programmers, computer scientists, and mathematicians try to create general rather than specific code whenever they can. It's a good habit because it forces us to think about solving problems in a more widely applicable way. If we do this, then our solutions can be applied to different scenarios. This means our code will keep many useful options open and assumptions to a minimum, so the code can easily be used for different needs. We want the same class to be able to create a small neural network as well as a very large one. We'll accomplish this by passing the desired size as parameters.

We also can't forget about the learning rate. That’s a useful parameter to set when we create a new neural network. So, let's see what the `__init__()` function might look like:

# Create the basic structure of a neural network
We know we need to set the number of input, hidden, and output layer nodes. This defines the shape and size of the neural network. We'll use parameters to set their values. This way, we retain the choice to create new neural networks of different sizes with ease.

There's an important point here. Good programmers, computer scientists, and mathematicians try to create general rather than specific code whenever they can. It's a good habit because it forces us to think about solving problems in a more widely applicable way. If we do this, then our solutions can be applied to different scenarios. This means our code will keep many useful options open and assumptions to a minimum, so the code can easily be used for different needs. We want the same class to be able to create a small neural network as well as a very large one. We'll accomplish this by passing the desired size as parameters.

We also can't forget about the learning rate. That’s a useful parameter to set when we create a new neural network. So, let's see what the `__init__()` function might look like:

Let's begin with the initialization of the neural network. 

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

Initialize the Neural Network

Create the basic structure of a neural network