Apache Spark Deep Learning Cookbook

Practical deep learning recipes for Apache Spark

Apache Spark Deep Learning Cookbook brings a hands-on, recipe-based approach to one of the most useful combinations in modern data work: distributed computing and deep learning. Instead of lingering on theory, the book moves quickly into real implementation, making it a strong fit for readers who want to build and test working solutions with Spark.

The material is structured around short, task-focused chapters. It begins with setting up Spark for deep learning development, then moves into neural networks and the core building blocks needed to work with data in PySpark. From there, the book expands into applied machine learning and deep learning techniques across a range of problems.

What the book covers

• Setting up a Spark environment for deep learning development
• Working with PySpark dataframes and arrays
• Building neural network workflows in Spark
• Using TensorFlow and Keras with Spark
• Exploring CNNs, RNNs, LSTMs, Word2Vec, TF-IDF, and XGBoost
• Applying Spark to practical use cases in NLP, computer vision, and prediction tasks

A cookbook for practitioners

The recipe format makes this title especially approachable. Each section is designed to solve a specific problem, which helps readers move through the book in a practical, methodical way. That approach is well suited to developers, data scientists, and ML practitioners who prefer learning by doing.

Who will find it useful?

This ebook is best suited to readers with a basic understanding of machine learning and Apache Spark who want to deepen their applied skills. If you are looking for a focused guide to distributed deep learning workflows, this title offers a clear and workmanlike path through the subject.

At Digital Delights, we like books that earn their keep on the screen. This one does exactly that: it is technical, practical, and built for readers who want to put Spark to work on real deep learning problems.