    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

# Test the network

Now that we’ve trained the network, at least on a small subset of 100 records, we want to test how well that worked. We'll do this against the second dataset, the training dataset. 

First we need to get the test records, and the Python code we'll use is very similar to what we used to get the training data:

# load the mnist test data CSV file into a list
test_data_file = open("mnist_test_10.csv", 'r')
test_data_list = test_data_file.readlines()
test_data_file.close()

We unpack this data the same way as before, because it has the same structure: 

# load the mnist test data CSV file into a list
test_data_file = open("mnist_test_10.csv", 'r')
test_data_list = test_data_file.readlines()
test_data_file.close()

#get the first test record
all_values = test_data_list[0].split(',')
#print the label
print(all_values[0])

Before we create a loop to go through all the test records, let’s see what happens if we manually run one test. The following code shows the tenth record from the test dataset being used to query the now-trained neural network:

image_array = numpy.asfarray(all_values[1:]).reshape((28,28))
matplotlib.pyplot.imshow(image_array, cmap = 'Greys', interpolation = 'None')
matplotlib.pyplot.savefig("output/samplePlot3.png")

We can see that the label for the 10th record from the test data set is $9$. That’s what we hope the neural network will answer when we query it. 

Plotting the pixel values as an image confirms the handwritten number is indeed a $9$. 

# Output scores of the trained network
Now, let's query the trained network to see what it shows:

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

Test the Network on a Subset

Test the network