Book Image

The Applied TensorFlow and Keras Workshop

By : Harveen Singh Chadha, Luis Capelo
Book Image

The Applied TensorFlow and Keras Workshop

By: Harveen Singh Chadha, Luis Capelo

Overview of this book

Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you’ll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you’ve understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you’ll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you’ll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you’ll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.
Table of Contents (6 chapters)

Choosing the Right Model Architecture

Considering the available architecture possibilities, there are two popular architectures that are often used as starting points for several applications: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These are foundational networks and should be considered starting points for most projects.

We also include descriptions of another three networks, due to their relevance in the field: Long Short-Term Memory (LSTM) networks (an RNN variant); Generative Adversarial Networks (GANs); and Deep Reinforcement Learning (DRL). These latter architectures have shown great success in solving contemporary problems, however, they are slightly difficult to use. The next section will cover the use of different types of architecture in different problems.

Convolutional Neural Networks (CNNs)

CNNs have gained notoriety for working with problems that have a grid-like structure. They were originally created to classify images,...