Guglielmo Iozzia - Hands-On Deep Learning with Apache Spark

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Guglielmo Iozzia is currently a big data delivery manager at Optum in Dublin. He completed his master's degree in biomedical engineering at the University of Bologna. After graduation, he joined a start-up IT company in Bologna that had implemented a new system to manage online payments. There, he worked on complex Java projects for different customers in different areas. He has also worked at the IT department of FAO, an agency of the United Nations. In 2013, he had the chance to join IBM in Dublin. There, he improved his DevOps skills, working mostly on cloud-based applications. He is a golden member, writes articles at DZone, and maintains a personal blog to share his findings and thoughts about various tech topics.

Nisith Kumar Nanda is a passionate big data consultant who loves to find solutions to complex data problems. He has around 10 years of IT experience from working on multiple technologies with various clients globally. His core expertise involves working with open source big data technologies such as Apache Spark, Kafka, Cassandra, and HBase to build critical next-generation real-time and batch applications. He is very proficient in various programming languages, such as Java, Scala, and Python. He is passionate about AI, machine learning, and NLP.

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Deep learning is a subset of machine learning based on multilayer neural networks that can solve particularly hard and large-scale problems in areas such as natural language processing and image classification. This book addresses the sheer complexity of the technical and analytical parts, and the speed at which deep learning solutions can be implemented on top of Apache Spark.

The book starts with an explanation of the fundamentals of Apache Spark and deep learning (how to set up Spark for deep learning, the principles of distributed modeling, and different types of neural network). Then it moves to the implementation of some deep learning models, such as CNNs, RNNs, and LSTMs, on Spark. The readers will get hands-on experience of what it takes and a general feeling of the complexity of what they are dealing with. During the course of the book, popular deep learning frameworks such as DeepLearning4J (mostly), Keras, and TensorFlow will be used to implement and train distributed models.

The mission of this book is as follows:

• To create a hands-on guide to implementing Scala (and in some cases, Python too) deep learning solutions that scale and perform
• To make readers confident with using Spark via several code examples
• To explain how to choose the model that best addresses a particular deep learning problem or scenario

If you are a Scala developer, a data scientist, or a data analyst who wants to learn how to use Spark to implement efficient deep learning models, this is the book for you. Knowledge of core machine learning concepts and some experience of using Spark will be helpful.

, The Apache Spark Ecosystem , provides a comprehensive overview of the Apache Spark modules and its different deployment modes.