English | 2022 | ISBN: ‎ 9391392083 | 554 pages | PDF | 18.04 MB

Explore the cosmic secrets of Distributed Processing for Deep Learning applications

Key Features
In-depth practical demonstration of ML/DL concepts using Distributed Framework.

Covers graphical illustrations and visual explanations for ML/DL pipelines.
Includes live codebase for each of NLP, computer vision and machine learning applications.

Description
This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark.

The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes.

Towards the end of the book, readers will learn about several deep learning frameworks, such as TensorFlow and PyTorch. Readers will also learn to execute distributed processing of deep learning problems using the Spark programming language.

What you will learn
Learn how to get started with machine learning projects using Spark.
Witness how to use Spark MLib's design for machine learning and deep learning operations.
Use Spark in tasks involving NLP, unsupervised learning, and computer vision.
Expent with Spark in a cloud environment and with AI pipeline workflows.
Run deep learning applications on a distributed network.

Who this book is for
This book is valuable for data eeers, machine learning eeers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python.

Table of Contents
1. Introduction to Machine Learning
2. Apache Spark Environment Setup and Configuration
3. Apache Spark
4. Apache Spark MLlib
5. Supervised Learning with Spark
6. Un-Supervised Learning with Apache Spark
7. Natural Language Processing with Apache Spark
8. Recommendation Ee with Distributed Framework
9. Deep Learning with Spark
10. Computer Vision with Apache Spark



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