    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

## How neurons really work
Let’s look at how a neuron works. It takes an electric input and sends out another electrical signal. This looks exactly like the classifying or predicting machines we looked at earlier, which took an input, did some processing, and produced an output. 

So, can we represent neurons as linear functions, like we did before? It's a good idea, but no. A biological neuron doesn’t produce an output that is a simple linear function of the input. That is, its output does not take the form:

$$\text{Output = (constant$\cdot$input) + (maybe)\hspace{0.4mm} 
 another\hspace{0.4mm}
 constant)} $$
$$$$
Observations suggest that neurons don’t react readily, but instead suppress the input until it has grown so large that it triggers an output. We can think of this as a threshold that must be reached before any output is produced. It’s like water in a cup—the water doesn’t spill over until it has first filled the cup. Intuitively, this makes sense. The neurons don’t want to pass tiny noise signals. They only want to pass emphatically strong, intentional signals. The following image illustrates this idea of producing an output signal only if the input is sufficiently dialed up to pass a threshold.

# How neurons really work
Let’s look at how a neuron works. It takes an electric input and sends out another electrical signal. This looks exactly like the classifying or predicting machines we looked at earlier, which took an input, did some processing, and produced an output. 

So, can we represent neurons as linear functions, like we did before? It's a good idea, but no. A biological neuron doesn’t produce an output that is a simple linear function of the input. That is, its output does not take the form:

$$\text{Output = (constant$\cdot$input) + (maybe)\hspace{0.4mm} 
 another\hspace{0.4mm}
 constant)} $$
$$$$
Observations suggest that neurons don’t react readily, but instead suppress the input until it has grown so large that it triggers an output. We can think of this as a threshold that must be reached before any output is produced. It’s like water in a cup—the water doesn’t spill over until it has first filled the cup. Intuitively, this makes sense. The neurons don’t want to pass tiny noise signals. They only want to pass emphatically strong, intentional signals. The following image illustrates this idea of producing an output signal only if the input is sufficiently dialed up to pass a threshold.

Learn how neurons work and how to excite a neuron when the signals cross a threshold.

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

How Neurons Work

How neurons really work