    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

## Code to plot data points 

We saw earlier how we might plot a rectangular array of numbers using the `imshow()` function. We want to do the same here, but we need to convert that list of comma-separated numbers into a suitable array. Here are the steps to do that:

1. Split that long text string of comma-separated values into individual values, using the commas as the place to do the splitting.

2. Ignore the first value and the label, and take the remaining list of $28 \times 28 = 784$ values and turn them into an array that has a shape of $28$ rows $\times$ $28$ columns.

3. Plot that array.

Again, it is easiest to show the simple Python code that does this and talk through the code to explain in more detail what is happening.

We must not forget to import the Python extension libraries first, which will help us with arrays and plotting:

# Code to plot data points 

We saw earlier how we might plot a rectangular array of numbers using the `imshow()` function. We want to do the same here, but we need to convert that list of comma-separated numbers into a suitable array. Here are the steps to do that:

1. Split that long text string of comma-separated values into individual values, using the commas as the place to do the splitting.

2. Ignore the first value and the label, and take the remaining list of $28 \times 28 = 784$ values and turn them into an array that has a shape of $28$ rows $\times$ $28$ columns.

3. Plot that array.

Again, it is easiest to show the simple Python code that does this and talk through the code to explain in more detail what is happening.

We must not forget to import the Python extension libraries first, which will help us with arrays and plotting:

Learn how digits are formed by plotting some of the data points.

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

Plot the Data Points

Code to plot data points