Deep Learning: A Practitioner's Approach

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

• Dive into machine learning concepts in general, as well as deep learning in particular
• Understand how deep networks evolved from neural network fundamentals
• Explore the major deep network architectures, including Convolutional and Recurrent
• Learn how to map specific deep networks to the right problem
• Walk through the fundamentals of tuning general neural networks and specific deep network architectures
• Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
• Learn how to use DL4J natively on Spark and Hadoop

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• Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Deep Learning with Python
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• Practical Statistics for Data Scientists: 50 Essential Concepts
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
• Neural Networks and Deep Learning: A Textbook