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)

From Data Preparation to Modeling

This section focuses on the implementation aspects of a deep learning system. We will use the Bitcoin data from the Choosing the Right Model Architecture section, and the Keras knowledge from the preceding section, Using Keras as a TensorFlow Interface, to put both of these components together. This section concludes the chapter by building a system that reads data from a disk and feeds it into a model as a single piece of software.

Training a Neural Network

Neural networks can take long periods of time to train. Many factors affect how long that process may take. Among them, three factors are commonly considered the most important:

  • The network's architecture
  • How many layers and neurons the network has
  • How much data there is to be used in the training process

Other factors may also greatly impact how long a network takes to train, but most of the optimization that a neural network can have when addressing a business...