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)

Using Keras as a TensorFlow Interface

We are using Keras because it simplifies the TensorFlow interface into general abstractions and, in TensorFlow 2.0, this is the default API in this version. In the backend, the computations are still performed in TensorFlow, but we spend less time worrying about individual components, such as variables and operations, and spend more time building the network as a computational unit. Keras makes it easy to experiment with different architectures and hyperparameters, moving more quickly toward a performant solution.

As of TensorFlow 2.0.0, Keras is now officially distributed with TensorFlow as tf.keras. This suggests that Keras is now tightly integrated with TensorFlow and will likely continue to be developed as an open source tool for a long period of time. Components are an integral part when building models. Let's deep dive into this concept now.

Model Components

As we saw in Chapter 1, Introduction to Neural Networks and Deep Learning...