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

Hands-On Deep Learning with Apache Spark: Build and deploy distributed deep learning applications on Apache Spark
Hands-On Deep Learning with Apache Spark: Build and deploy distributed deep learning applications on Apache Spark by Guglielmo Iozzia
English | 2019 | ISBN: 1788994613 | 322 Pages | EPUB | 21 MB

Speed up the design and implementation of deep learning solutions using Apache Spark
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