    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

# Sample of MNIST test data  
Let's take a peek at these files. The following image shows a section of the MNIST test set loaded into a text editor.



# Format of data file
The text editor is showing long lines of text, consisting of comma-separated numbers. The lines are quite long, so they wrap around a few times. This text editor helpfully shows the real line numbers in the margin, so we can see four whole lines of data and part of the fifth one.

The content of these records or lines of text are easy to understand:

- The first value is the label, that is, the actual digit that the handwriting is supposed to represent, such as a $7$ or a $9$. This is the answer the neural network is trying to learn how to get right.

- The subsequent comma-separated values are the pixel values of the handwritten digit. The size of the pixel array is $28 \times 28$, so there are $784$ values after the label.

The first record represents the number $5$, as shown by the first value, and the rest of the text on that line are the pixel values for someone's handwritten number $5$. The second record represents a handwritten $0$, the third represents $4$, the fourth record is $1$, and the fifth represents $9$. We can pick any line from the MNIST data files, and the first number will tell us the label for the following image data.

However, it is hard to see how that long list of $784$ values makes up a picture of someone's handwritten number $5$. We should plot those numbers as an image to confirm that they really are the color values of the handwritten number.

Before we dive in and do that, we should download a smaller subset of the MNIST dataset. The MNIST data files are pretty big and working with a smaller subset is helpful because it means we can experiment, trial, and develop our code without a large data set slowing our computers down. Once we've settled on an algorithm and code we're happy with, we can use the full dataset.

The following files is a smaller subset of the MNIST dataset, also in CSV format:

- 10 records from the MNIST test data set

Understand the data files of MNIST handwritten digits database. 

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

MNIST Data File

Sample of MNIST test data

Format of data file