by Tomasz Lelek | Released January 2019 | ISBN: 9781787286689

Develop fast, efficient distributed deep learning models with Apache SparkAbout This VideoUse deep learning method with Apache Spark to stay on the cutting edge of ML techniquesUnderstand DL neural networks with versatile code and examples from the real worldLearn about deep learning algorithms running on the DL4J framework and how they compare with other popular DL frameworksIn DetailDeep Learning is a subset of Machine Learning whereby datasets with several layers of complexity can be processed efficiently. This tutorial brings together two of the most popular buzzwords of today—big data and Artificial Intelligence—by showing you how you can implement Deep Learning solutions using the power of Apache Spark.The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed CPU and GPU setup.By the end of this course, you'll have gained experience by implementing models for applications such as object recognition, text analysis, and voice recognition. You will even have designed human expert games.The code bundle for this course is available at https://github.com/PacktPublishing/Deep-Learning-with-Apache-SparkDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : The Fundamentals of Apache Spark and Deep Learning
   • The Course Overview 00:02:06
   • Review of Key Machine Learning Terminology and Fundamentals 00:04:10
   • Fundamentals of Deep Networks: Feature Engineering 00:03:11
   • The Building Blocks of Deep Learning 00:04:47
   • Learning Path for Deep Learning 00:02:23
   • Deep Learning Use Cases 00:03:09
2. Chapter 2 : Up and Running with the Spark Environment for Performing Deep Learning
   • Pre-requisites and Installation 00:03:44
   • Up and Running with DL4J on Spark 00:04:12
   • Configuration and Test Run 00:05:51
   • Up and Running with TensorFlow on Spark from Yahoo 00:04:00
3. Chapter 3 : Hands-On with the DL4J Ecosystem
   • Understanding the Basics of Deep Learning 00:06:57
   • ND4J for NumPy-like Arrays and Operations 00:09:27
   • Data.Vec for Data Preparation Pipelines 00:04:54
   • DL4J for Building Neural Network Architectures 00:03:47
4. Chapter 4 : GPU Distributed Training and CNN
   • Understanding the Basics of GPU 00:05:42
   • Parallel Training with Multiple GPUs 00:04:24
   • Designing a Basic CNN 00:04:53
   • Implement a Basic CNN on DL4J in Spark 00:05:10
5. Chapter 5 : Recurrent Neural Networks (RNN) and LSTMs
   • Basics and Design of RNN 00:02:44
   • Implement a Basic RNN on DL4J in Spark 00:04:53
   • Design a Basic LSTM 00:04:37
   • Implement a Basic LSTM in Spark 00:05:25
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