    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

# Maintain the score of each record
Let’s keep going and write the code to see how the neural network performs against the rest of the dataset, and keep a score so we can see later if our own ideas for improving the learning worked and also to compare with how well others have done this. 

Before we jump into the loop that works through all the test dataset records, we'll create an empty list called `scorecard`, which will be the scorecard that we update after each record.


# test the neural network

# scorecard for how well the network performs, initially empty
scorecard = []
# go through all the records in the test data set
for record in test_data_list:
    # split the record by the ',' commas
    all_values = record.split(',')
    # correct answer is first value
    correct_label = int(all_values[0])
    print(correct_label, "correct label")
    # scale and shift the inputs
    inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
    # query the network
    outputs = n.query(inputs)
    # the index of the highest value corresponds to the label
    label = numpy.argmax(outputs)
    print(label, "network's answer")
    # append correct or incorrect to list
    if (label == correct_label):
        # network's answer matches correct answer, add 1 to scorecard
        scorecard.append(1)
    else:
        # network's answer doesn't match correct answer, add 0 to scorecard
        scorecard.append(0)
        pass
    
    pass

# Code explanation

**Lines 9–16:** We can see that inside the loop, we do what we did before. We split the text record by the commas to separate out the values. We keep a note of the first value as the correct answer. We grab the remaining values and rescale them so they're suitable for querying the neural network. And we keep the response from the neural network in a variable called `outputs`.

**Line 18:** We know the output node with the largest value is the one the network thinks is the answer. The index of that node, that is, its position, corresponds to the label. That's a long way of saying the first element corresponds to the label $0$, and the fifth element corresponds to the label $4$, and so on. Luckily, there is a convenient NumPy function that finds the largest value in an array and tells us its position, called `numpy.argmax()`. If it returns $0$, we know the network thinks the answer is zero, and so on.

**Lines 21–26:** That last bit of code compares the label with the known correct label. If they are the same, a `1` is appended to the scorecard. Otherwise, a `0` is appended. 

We've included some useful `print()` commands in the code so we can see for ourselves the correct and predicted labels. The following shows the results of this code and also of printing out the scorecard:

Test the neural network performance based on the score of each record.

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 the Whole Dataset

Maintain the score of each record