    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.

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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

# The need to rescale the output
We now need to think about the neural network’s outputs. We saw earlier that the outputs should match the range of values that the activation function can push out. The logistic function we’re using can’t push out numbers like $-2.0$ or $255$. The range is $0.0–1.0$, and in fact, we can’t reach $0.0$ or $1.0$ because the logistic function can only approach these extremes and will never actually get there. So, it looks like we’ll have to scale our target values when training.

What should the output even be? Should it be an image of the answer? That would mean we'd have $28\times28 = 784$ output nodes. 

If we take a step back and think about what we’re asking the neural network to do, we realize we’re asking it to classify the image and assign the correct label. That label is one of $10$ numbers, or $0–9$. That means the network should be able to have an output layer of $10$ nodes, one for each of the possible answers, or labels. If the answer is $0$, the first output layer node would fire and the rest should be silent. If the answer is $9$, the last output layer node would fire and the rest would be silent.
# Examples 
The following table illustrates this scheme with some example outputs:


Learn the importance of rescaling the input and output values to achieve good results.

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

Rescale the Target Output

The need to rescale the output