Sherif / Ravindra / Malohlava Apache Spark Deep Learning Cookbook

Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow
1. Auflage 2024
ISBN: 978-1-78847-155-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 474 Seiten

Run efficient deep learning models on Apache Spark using TensorFlow and KerasKey Features - Train distributed complex neural networks on Apache Spark
- Use TensorFlow and Keras to train and deploy deep learning models
- Explore practical tips to enhance performance
Book 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 learn - Set up a fully functional Spark environment
- Understand practical machine learning and deep learning concepts
- Employ built-in machine learning libraries within Spark
- Discover 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 can be reused
Who 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.

Autoren/Hrsg.

Sherif, Ahmed

Ravindra, Amrith

Malohlava, Michal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

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

Über Autor(innen)

Sherif Ahmed :

Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.Ravindra Amrith :

Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his masters, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.