Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
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Chapter 1. Getting Started with Deep Learning

This chapter explains some of the basic concepts of Machine Learning (ML) and Deep Learning (DL) that will be used in all the subsequent chapters. We will start with a brief introduction to ML. Then we will move to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.

We will briefly discuss some of the most well-known and widely used neural network architectures, before moving on to coding with TensorFlow in Chapter 2, A First Look at TensorFlow. In this chapter, we will look at various features of DL frameworks and libraries, such as the native language of the framework, multi-GPU support, and aspects of usability.

In a nutshell, the following topics will be covered:

  • A soft introduction to ML

  • Artificial neural networks

  • ML versus DL

  • DL neural network architectures

  • Available DL frameworks