Deep Learning: Practical Neural Networks with Java

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Packt Publishing
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Jun, 2017

About Deep Learning: Practical Neural Networks with Java

Build and run intelligent applications by leveraging key Java machine learning libraries

About This Book

  • Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.

  • Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications

  • This step-by-step guide will help you solve real-world problems and links neural network theory to their application

Who This Book Is For

This course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life.

What You Will Learn

  • Get a practical deep dive into machine learning and deep learning algorithms

  • Explore neural networks using some of the most popular Deep Learning frameworks

  • Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms

  • Apply machine learning to fraud, anomaly, and outlier detection

  • Experiment with deep learning concepts, algorithms, and the toolbox for deep learning

  • Select and split data sets into training, test, and validation, and explore validation strategies

  • Apply the code generated in practical examples, including weather forecasting and pattern recognition

In Detail

Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.
The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:

  1. Java Deep Learning Essentials

  2. Machine Learning in Java

  3. Neural Network Programming with Java, Second Edition

Style and approach

This course aims to create a smooth learning path that will teach you how to effectively use deep learning with Java with other de facto components to get the most out of it. Through this comprehensive course, you'll learn the basics of predictive modelling and progress to solve real-world problems and links neural network theory to their application

Table of Contents

Part 1. Java Deep Learning Essentials
Chapter 1. Deep Learning Overview
Chapter 2. Algorithms For Machine Learning – Preparing For Deep Learning
Chapter 3. Deep Belief Nets And Stacked Denoising Autoencoders
Chapter 4. Dropout And Convolutional Neural Networks
Chapter 5. Exploring Java Deep Learning Libraries – Dl4J, Nd4J, And More
Chapter 6. Approaches To Practical Applications – Recurrent Neural Networks And More
Chapter 7. Other Important Deep Learning Libraries
Chapter 8. What'S Next?
Part 2. Machine Learning In Java
Chapter 1. Applied Machine Learning Quick Start
Chapter 2. Java Libraries And Platforms For Machine Learning
Chapter 3. Basic Algorithms – Classification, Regression, And Clustering
Chapter 4. Customer Relationship Prediction With Ensembles
Chapter 5. Affinity Analysis
Chapter 6. Recommendation Engine With Apache Mahout
Chapter 7. Fraud And Anomaly Detection
Chapter 8. Image Recognition With Deeplearning4J
Chapter 9. Activity Recognition With Mobile Phone Sensors
Chapter 10. Text Mining With Mallet – Topic Modeling And Spam Detection
Chapter 11. What Is Next?
Part 3. Neural Network Programming With Java, Second Edition
Chapter 1. Getting Started With Neural Networks
Chapter 2. Getting Neural Networks To Learn
Chapter 3. Perceptrons And Supervised Learning
Chapter 4. Self-Organizing Maps
Chapter 5. Forecasting Weather
Chapter 6. Classifying Disease Diagnosis
Chapter 7. Clustering Customer Profiles
Chapter 8. Text Recognition
Chapter 9. Optimizing And Adapting Neural Networks
Chapter 10. Current Trends In Neural Networks

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