Hands-On Deep Learning with Apache Spark - Guglielmo Iozzia

Hands-On Deep Learning with Apache Spark

Build and deploy distributed deep learning applications on Apache Spark
Buch | Softcover
322 Seiten
2019
Packt Publishing Limited (Verlag)
978-1-78899-461-3 (ISBN)
44,85 inkl. MwSt
Deep Learning is a subset of Machine Learning where data sets with several layers of complexity can be processed. This book teaches you the different techniques using which deep learning solutions can be implemented at scale, on Apache Spark. This will help you gain experience of implementing your deep learning models in many real-world use cases.
Speed up the design and implementation of deep learning solutions using Apache Spark

Key Features

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

Book DescriptionDeep 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

Who this book is forIf you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.

Guglielmo Iozzia is currently a big data delivery manager at Optum in Dublin. He completed his master's degree in biomedical engineering at the University of Bologna. After graduation, he joined a start-up IT company in Bologna that had implemented a new system to manage online payments. There, he worked on complex Java projects for different customers in different areas. He has also worked at the IT department of FAO, an agency of the United Nations. In 2013, he had the chance to join IBM in Dublin. There, he improved his DevOps skills, working mostly on cloud-based applications. He is a golden member, writes articles at DZone, and maintains a personal blog to share his findings and thoughts about various tech topics.

Table of Contents

The Apache Spark Ecosystem
Deep Learning Basics
Extract, Transform, Load
Streaming
Convolutional Neural Networks
Recurrent Neural Networks
Training Neural Networks with Spark
Monitoring and Debugging Neural Network Training
Interpreting Neural Network Output
Deploying on a Distributed System
NLP Basics
Textual Analysis and Deep Learning
Convolution
Image Classification
What’s Next for Deep Learning?

Erscheinungsdatum
Verlagsort Birmingham
Sprache englisch
Maße 75 x 93 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
ISBN-10 1-78899-461-2 / 1788994612
ISBN-13 978-1-78899-461-3 / 9781788994613
Zustand Neuware
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