    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

# Open the dataset 
Before we can do anything with the data, like plotting it or training a neural network with it, we need to find a way to get at it from our Python code.

Opening a file and getting its content is easy in Python. Let's look at the following code:


data_file = open("mnist_train_100.csv", 'r')
data_list = data_file.readlines()
data_file.close()

There are only three lines of code here. Let's go through each one.

# Code explanation
## Open the file
The first line uses a function `open()` to open a file. We can see the first parameter passed to the function is the name of the file. Actually, it is more than just the filename `mnist_train_100.csv`, it is the whole path, which includes the directory of the file. 

The second parameter is optional, and tells Python how we want to treat the file. The `'r'` parameter tells Python that we want to open the file for reading only. This way, we avoid any accidents that change or delete the data. If we tried to write to the file and change it, Python would stop us and raise an error. 

What's that variable `data_file`? The `open()` function creates a file handle, a reference, to that file and we've assigned it to a variable named `data_file`. Now that we've opened the file, any further actions like reading from it are done through that handle.

## Read the file
The next line is simple. We use the `readlines()` function associated with the file handle `data_file` to read all of the lines in the file into the variable `data_list`. The variable contains a list, where each item of the list is a string representing a line in the file. That's much more useful because we can jump to specific lines, just like we would jump to specific entries in a list. So `data_list[0]` is the first record, and `data_list[9]` is the tenth record, and so on.

By the way, we may hear people tell us not to use `readlines()` because it reads the entire file into memory. They will tell us to read one line at a time and do whatever work we need with each line, and then move onto the next line. They aren't wrong. It is more efficient to work on one line at a time, and not read the entire file into memory. However, our files aren't that massive, and the code is easier if we use `readlines()`.

## Close the file
The last line closes the file. It is good practice to close and clean up after using resources like files. If we don't, they remain open and can cause problems. What problems? Well, some programs might not want to write to a file that was opened somewhere else in case it leads to an inconsistency. It would be like two people trying to write a letter on the same piece of paper! Sometimes our computer will lock a file to prevent this kind of clash. If we don't clean up after we use files, we can have a buildup of locked files. At the very least, closing a file allows our computer to free up memory it used to hold parts of that file.

# Print the dataset 
Let's try this code, and see what happens when we print out elements of that list. Look at the following code:

Explore how to access and use the dataset file in Python.

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

Get the Dataset Ready

Open the dataset

Code explanation

Open the file