Apache Spark Deep Learning Cookbook
Language: English
Published by Packt Publishing Limited, GB, 2018
- Softcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
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Add to basketItem description from seller
Run efficient deep learning models on Apache Spark using TensorFlow and KerasKey FeaturesTrain distributed complex neural networks on Apache SparkUse TensorFlow and Keras to train and deploy deep learning modelsExplore practical tips to enhance performanceBook DescriptionOrganizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they're looking to gain faster and more powerful insights from their data. With this book, you'll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You'll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.What you will learnSet up a fully functional Spark environmentUnderstand practical machine learning and deep learning conceptsEmploy built-in machine learning libraries within SparkDiscover libraries that are compatible with TensorFlow and KerasExplore NLP models such as word2vec and TF-IDF on SparkOrganize DataFrames for deep learning evaluationApply testing and training modeling to ensure accuracyAccess readily available code that can be reusedWho this book is forIf you're looking for a practical resource for implementing efficiently distributed deep learning models with Apache Spark, then this book is for you. Knowledge of core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the most out of this book. Some knowledge of Python programming will also be useful.…
Seller Inventory # LU-9781788474221
- Title
- Apache Spark Deep Learning Cookbook
- Author
- Ahmed Sherif, Amrith Ravindra, Adnan Masood, Michal Malohlava
- Publisher
- Packt Publishing Limited, GB
- Publication year
- 2018
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1788474228
- ISBN 13
- 9781788474221
A solution-based guide to put your deep learning models into production with the power of Apache Spark
Key Features
- Discover practical recipes for distributed deep learning with Apache Spark
- Learn to use libraries such as Keras and TensorFlow
- Solve problems in order to train your deep learning models on Apache Spark
Book Description
With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed.
With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras.
By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.
What you will learn
- Set up a fully functional Spark environment
- Understand practical machine learning and deep learning concepts
- Apply built-in machine learning libraries within Spark
- Explore libraries that are compatible with TensorFlow and Keras
- Explore NLP models such as Word2vec and TF-IDF on Spark
- Organize dataframes for deep learning evaluation
- Apply testing and training modeling to ensure accuracy
- Access readily available code that may be reusable
Who this book is for
If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.
Table of Contents
- Setting Up Spark for Deep Learning Development
- Creating a Neural Network in Spark
- Pain Points of Convolutional Neural Networks
- Pain Points of Recurrent Neural Networks
- Predicting Fire Department Calls with Spark ML
- Using LSTMs in Generative Networks
- Natural Language Processing with TF-IDF
- Real Estate Value Prediction using XGBoost
- Predicting Apple Stock Market Cost with LSTM
- Face Recognition using Deep Convolutional Networks
- Creating and Visualizing Word Vectors Using Word2Vec
- Creating a Movie Recommendation Engine with Keras
- Image Classification with TensorFlow on Spark
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