Hands-On Deep Learning with Apache Spark: Build and deploy distributed deep learning applications on Apache Spark

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Deep Learning Basics

In this chapter, I am going to introduce the core concepts of Deep Learning (DL), the relationship it has with Machine Learning (ML) and Artificial Intelligence (AI), the different types of multilayered neural networks, and a list of real-world practical applications. I will try to skip mathematical equations as much as possible and keep the description very high level, with no reference to code examples. The goal of this chapter is to make readers aware of what DL really is and what you can do with it, while the following chapters will go much more into the details of this, with lots of practical code examples in Scala and Python (where this programming language can be used).

This chapter will cover the following topics:

DL concepts
Deep neural networks (DNNs)
Practical applications of DL

Key benefits

Explore the world of distributed deep learning with Apache Spark

Train neural networks with deep learning libraries such as BigDL and TensorFlow

Develop Spark deep learning applications to intelligently handle large and complex datasets

Description

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.

What you will learn

Understand the basics of deep learning

Set up Apache Spark for deep learning

Understand the principles of distribution modeling and different types of neural networks

Obtain an understanding of deep learning algorithms

Discover textual analysis and deep learning with Spark

Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras

Explore popular deep learning algorithms

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