Deep Learning with Hadoop
Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Database Management, Data Processing
| Author: | Dipayan Dev | ISBN: | 9781787121232 |
| Publisher: | Packt Publishing | Publication: | February 20, 2017 |
| Imprint: | Packt Publishing | Language: | English |
| Author: | Dipayan Dev |
| ISBN: | 9781787121232 |
| Publisher: | Packt Publishing |
| Publication: | February 20, 2017 |
| Imprint: | Packt Publishing |
| Language: | English |
Build, implement and scale distributed deep learning models for large-scale datasets
About This Book
- Get to grips with the deep learning concepts and set up Hadoop to put them to use
- Implement and parallelize deep learning models on Hadoop's YARN framework
- A comprehensive tutorial to distributed deep learning with Hadoop
Who This Book Is For
If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.
What You Will Learn
- Explore Deep Learning and various models associated with it
- Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
- Implement Convolutional Neural Network (CNN) with deeplearning4j
- Delve into the implementation of Restricted Boltzmann Machines (RBM)
- Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
- Get hands on practice of deep learning and their implementation with Hadoop.
In Detail
This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance.
Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j.
Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop.
By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.
Style and approach
This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.
Build, implement and scale distributed deep learning models for large-scale datasets
About This Book
- Get to grips with the deep learning concepts and set up Hadoop to put them to use
- Implement and parallelize deep learning models on Hadoop's YARN framework
- A comprehensive tutorial to distributed deep learning with Hadoop
Who This Book Is For
If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.
What You Will Learn
- Explore Deep Learning and various models associated with it
- Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
- Implement Convolutional Neural Network (CNN) with deeplearning4j
- Delve into the implementation of Restricted Boltzmann Machines (RBM)
- Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
- Get hands on practice of deep learning and their implementation with Hadoop.
In Detail
This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance.
Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j.
Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop.
By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.
Style and approach
This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.
