    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

# Linear classifier

We want to train our linear classifier to correctly classify bugs as ladybugs or caterpillars. As we saw before, this is simply about refining the slope of the dividing line that separates the two groups of points on a plot showing width and height.

Rather than developing a mathematical theory upfront, let's try to get a feel for what we need to do by trying to do it. We'll understand the mathematics better that way. We do need some examples to learn from. The following table shows two simple examples.

| **Example** | **Width** | **Length** | **Bug** |
|:-----------:|:-----------:|:-----------:|:-----------:|
| 1 | 3.0 | 1.0 | Ladybug |
| 2 | 1.0 | 3.0 | Caterpillar |

We have one example of a bug that has a width of $3.0$ and a length of $1.0$, which we know is a ladybug. We also have an example of a bug that is $3.0$ long and $1.0$ wide, which is a caterpillar. This is a set of examples that we know to be the truth. It is these examples that will help refine the slope of the classifier function. Examples of truth, used to teach a predictor or a classifier, are called **training data**.

Let's plot these two training data examples. Visualizing data is often very helpful to get a better understanding of it, which isn't easy to get just by looking at a list or table of numbers.

Learn how to build a linear classifier with the help of training data examples.

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

Build a Simple Classifier

Linear classifier