    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

## Weights 
Let's create the network of nodes and links. The most important part of the network is the link weights. They’re used to calculate the signal being fed forward, the error as it’s propagated backward, and the link weights themselves are refined in an attempt to improve the network.

The weights can be concisely expressed as a matrix. So, we can create:

- A matrix for the weights for links between the input and hidden layers: $W_\text{input\_hidden}$, of size $(\text{hidden\_nodes} \times \text{input\_nodes})$.

- A matrix for the links between the hidden and output layers: $W_\text{hidden\_output}$, of size $
(\text{output\_nodes} \times\text{hidden\_nodes})$.

Remember, earlier the convention was to see why the first matrix is set up as $(\text{hidden\_nodes} \times \text{input\_nodes})$, and not the other way around $(\text{input\_nodes} \times \text{hidden\_nodes})$.

Remember that the initial values of the link weights should be small and random. The following `numpy` function generates an array of values selected randomly between $0$ and $1$, where the size is (`rows` $\times$ `columns`).

# Weights 
Let's create the network of nodes and links. The most important part of the network is the link weights. They’re used to calculate the signal being fed forward, the error as it’s propagated backward, and the link weights themselves are refined in an attempt to improve the network.

The weights can be concisely expressed as a matrix. So, we can create:

- A matrix for the weights for links between the input and hidden layers: $W_\text{input\_hidden}$, of size $(\text{hidden\_nodes} \times \text{input\_nodes})$.

- A matrix for the links between the hidden and output layers: $W_\text{hidden\_output}$, of size $
(\text{output\_nodes} \times\text{hidden\_nodes})$.

Remember, earlier the convention was to see why the first matrix is set up as $(\text{hidden\_nodes} \times \text{input\_nodes})$, and not the other way around $(\text{input\_nodes} \times \text{hidden\_nodes})$.

Remember that the initial values of the link weights should be small and random. The following `numpy` function generates an array of values selected randomly between $0$ and $1$, where the size is (`rows` $\times$ `columns`).

Learn how to define link weights in a 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

Weights: The Heart of the Network

Weights