    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 classification for logical functions
Imagine using a simple linear classifier to learn from training data whether it's governed by a boolean logic function or not. That's a natural and useful thing to do for scientists who want to find causal links or correlations between some observations and others. For example, is there more malaria when it rains AND it is hotter than 35 degrees? Or is there more malaria when either (OR) of these conditions are true? 

Let's look at the following plot, showing the two inputs $A$ and $B$ as coordinates on a graph. The plot shows that only when both are true (with a value of $1$) is the output (shown as green) also true. (False outputs are shown red.)

We can also see a straight line that divides the red from the green regions. That line is a linear function that a linear classifier could learn, just as we did earlier. 

We won’t go through the numerical workings, as we did before, because they’re not fundamentally different in this example. In fact, there are many variations on this dividing line that would work just as well, but the main point is that it is indeed possible for a simple linear classifier of the form $y = ax+b$ to learn the AND function. 

Now, let's look at the OR function plotted in a similar way.


Discuss linear classifiers for different boolean functions, such as AND, OR, and XOR.

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

Represent Boolean Functions with Linear Classification

Linear classification for logical functions