    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

## Construction of a target array 

Now we need to turn these ideas into target arrays for the neural network training. We can see that if the label for a training example is $5$, we need to create a target array for the output node where all the elements are small except the one corresponding to the label $5$. That could look like:
$$
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
$$

In fact, we need to rescale those numbers because we’ve already seen how trying to get the neural network to create outputs $0$ and $1$, which are impossible for the activation function, will drive large weights and a saturated network. So, we’ll use the values $0.01$ and $0.99$ instead, so the target for the label $5$ should be:
$$
[0.01, 0.01, 0.01, 0.01, 0.01, 0.99, 0.01, 0.01, 0.01, 0.01]
$$ 

Let's have a look at the following Python code to construct the target matrix:


# Construction of a target array 

Now we need to turn these ideas into target arrays for the neural network training. We can see that if the label for a training example is $5$, we need to create a target array for the output node where all the elements are small except the one corresponding to the label $5$. That could look like:
$$
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
$$

In fact, we need to rescale those numbers because we’ve already seen how trying to get the neural network to create outputs $0$ and $1$, which are impossible for the activation function, will drive large weights and a saturated network. So, we’ll use the values $0.01$ and $0.99$ instead, so the target for the label $5$ should be:
$$
[0.01, 0.01, 0.01, 0.01, 0.01, 0.99, 0.01, 0.01, 0.01, 0.01]
$$ 

Let's have a look at the following Python code to construct the target matrix:


Learn how to create the target array for the MNIST dataset.

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

Create a Target Array

Construction of a target array