Java Deep Learning Essentials 1st Edition by Sugomori Yusuke ISBN 1785282190 9781785282195

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ISBN 10:    1785282190
ISBN 13: 978-1785282195
Author:   Sugomori Yusuke

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It’s something that’s moving beyond the realm of data science – if you’re a Java developer, this book gives you a great opportunity to expand your skillset.

Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you’ve got to grips with the fundamental mathematical principles, you’ll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you’ll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today.

By the end of the book, you’ll be ready to tackle Deep Learning with Java. Wherever you’ve come from – whether you’re a data scientist or Java developer – you will become a part of the Deep Learning revolution!

What You Will Learn

Who this book is for

This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment.

Java Deep Learning Essentials 1st Table of contents:

1. Deep Learning Overview

• Transition of AI: Exploring how AI has evolved over time.
• Definition of AI: What artificial intelligence is and how it functions.
• AI Booms in the Past: Significant advancements in AI throughout history.
• Machine Learning Evolves: How machine learning developed and contributed to modern AI.
• What Even Machine Learning Cannot Do: Limitations of machine learning techniques.
• Things Dividing a Machine and Human: Key differences between human intelligence and machine intelligence.
• AI and Deep Learning: A look at how deep learning fits within the broader field of AI.
• Summary: Key takeaways.

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2. Algorithms for Machine Learning – Preparing for Deep Learning

• Getting Started: Introduction to machine learning basics.
• The Need for Training in Machine Learning: The importance of data for training models.
• Supervised and Unsupervised Learning: Differences and use cases.
• Support Vector Machine (SVM): Overview and applications.
• Hidden Markov Model (HMM): Introduction to HMMs in machine learning.
• Neural Networks: Basic understanding of neural networks.
• Logistic Regression: Explanation and uses.
• Reinforcement Learning: Introduction to reinforcement learning techniques.
• Machine Learning Application Flow: How to implement a machine learning pipeline.
• Theories and Algorithms of Neural Networks: Overview of neural network models.
• Perceptrons (Single-layer Neural Networks): The foundation of neural networks.
• Logistic Regression: Explanation and applications.
• Multi-class Logistic Regression: Extending logistic regression for more classes.
• Multi-layer Perceptrons (Multi-layer Neural Networks): Introduction to deeper networks.
• Summary: Key takeaways.

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3. Deep Belief Nets and Stacked Denoising Autoencoders

• Neural Networks Fall: Discussing the challenges neural networks faced.
• Neural Networks’ Revenge: How neural networks regained prominence.
• Deep Learning’s Evolution – What Was the Breakthrough?: Key breakthroughs in deep learning.
• Deep Learning with Pre-training: The role of pre-training in deep learning.
• Deep Learning Algorithms: Various algorithms used in deep learning.
• Restricted Boltzmann Machines: Introduction and applications.
• Deep Belief Nets (DBNs): Overview and usage.
• Denoising Autoencoders: Techniques for cleaning data.
• Stacked Denoising Autoencoders (SDA): Advanced use of autoencoders.
• Summary: Key takeaways.

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4. Dropout and Convolutional Neural Networks

• Deep Learning Algorithms Without Pre-training: Approaches without pre-training.
• Dropout: Understanding the dropout technique for regularization.
• Convolutional Neural Networks (CNNs): Introduction to CNNs.
• Convolution: The process of applying convolutions in CNNs.
• Pooling: Explanation of pooling layers in CNNs.
• Equations and Implementations: Mathematical concepts and practical implementations.
• Summary: Key takeaways.

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5. Exploring Java Deep Learning Libraries – DL4J, ND4J, and More

• Implementing from Scratch vs. Using a Library/Framework: Comparison of building models from scratch or using pre-built libraries.
• Introducing DL4J and ND4J: Overview of the Java libraries.
• Implementations with ND4J: Working with ND4J for deep learning.
• Implementations with DL4J: Using DL4J for deep learning applications.
• Setup: Steps for setting up the libraries.
• Build: How to build projects with DL4J and ND4J.
• DBNIrisExample.java: Example code.
• CSVExample.java: Example code.
• CNNMnistExample.java/LenetMnistExample.java: Example for CNNs with MNIST dataset.
• Learning Rate Optimization: Tuning learning rates for better performance.
• Summary: Key takeaways.

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6. Approaches to Practical Applications – Recurrent Neural Networks and More

• Fields Where Deep Learning is Active: Examples of deep learning applications.
• Image Recognition: Deep learning’s impact on image recognition.
• Natural Language Processing (NLP): Application of deep learning in language tasks.
• Feed-forward Neural Networks for NLP: Using basic neural networks for language.
• Deep Learning for NLP: Advanced techniques for NLP.
• Recurrent Neural Networks (RNNs): Introduction and applications.
• Long Short-Term Memory Networks (LSTMs): Understanding LSTMs for sequence data.
• The Difficulties of Deep Learning: Challenges and limitations.
• Approaches to Maximizing Deep Learning’s Potential: Maximizing the capabilities of deep learning.
• Field-Oriented Approach: Applying deep learning to various industries.
• Medicine, Automobiles, Advert Technologies, Professions: Specific examples of deep learning applications.
• Breakdown-Oriented and Output-Oriented Approaches: Different ways to tackle deep learning problems.
• Summary: Key takeaways.

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7. Other Important Deep Learning Libraries

• Theano: Overview of Theano library.
• TensorFlow: Overview of TensorFlow and its capabilities.
• Caffe: Introduction to Caffe for deep learning.
• Summary: Key takeaways.

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8. What’s Next?

• Breaking News About Deep Learning: Latest updates in deep learning.
• Expected Next Actions: What to expect in the future of deep learning.
• Useful News Sources for Deep Learning: Sources for staying updated on the field.

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