Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals

Lesson 7: Deep Learning with Neural Networks

Activity 14: Written digit detection

  1. This section will discuss how to provide more security for the cryptocurrency traders via the detection of hand-written digits. We will be using assuming that you are a software developer at a new Cryptocurrency trader platform. The latest security measure you are implementing requires the recognition of hand-written digits. Use the MNIST library to train a neural network to recognize digits. You can read more about this dataset on

  2. Improve the accuracy of the model as much as possible. And to ensure that it happens correctly, you will need to complete the previous topic.

  3. Load the dataset and format the input

    import tensorflow.keras.datasets.mnist as mnist
    (features_train, label_train),
    (features_test, label_test) = mnist.load_data()
    features_train = features_train / 255.0
    features_test = features_test / 255.0
    def flatten(matrix): 
        return [elem for row in matrix for...