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Java Deep Learning Projects
Java Deep Learning Projects

Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

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Java Deep Learning Projects

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Key benefits

  • Understand DL with Java by implementing real-world projects
  • Master implementations of various ANN models and build your own DL systems
  • Develop applications using NLP, image classification, RL, and GPU processing

Description

Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.

Who is this book for?

If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.

What you will learn

  • Master deep learning and neural network architectures
  • Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs
  • Train ML agents to learn from data using deep reinforcement learning
  • Use factorization machines for advanced movie recommendations
  • Train DL models on distributed GPUs for faster deep learning with Spark and DL4J
  • Ease your learning experience through 69 FAQs

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 29, 2018
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781788997454
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Product Details

Publication date : Jun 29, 2018
Length: 436 pages
Edition : 1st
Language : English
ISBN-13 : 9781788997454
Category :
Languages :
Concepts :

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Frequently bought together


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Total $ 124.97
Hands-On Artificial Intelligence with Java for Beginners
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Java Deep Learning Projects
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Total $ 124.97 Stars icon

Table of Contents

12 Chapters
Getting Started with Deep Learning Chevron down icon Chevron up icon
Cancer Types Prediction Using Recurrent Type Networks Chevron down icon Chevron up icon
Multi-Label Image Classification Using Convolutional Neural Networks Chevron down icon Chevron up icon
Sentiment Analysis Using Word2Vec and LSTM Network Chevron down icon Chevron up icon
Transfer Learning for Image Classification Chevron down icon Chevron up icon
Real-Time Object Detection using YOLO, JavaCV, and DL4J Chevron down icon Chevron up icon
Stock Price Prediction Using LSTM Network Chevron down icon Chevron up icon
Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks Chevron down icon Chevron up icon
Playing GridWorld Game Using Deep Reinforcement Learning Chevron down icon Chevron up icon
Developing Movie Recommendation Systems Using Factorization Machines Chevron down icon Chevron up icon
Discussion, Current Trends, and Outlook Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(4 Ratings)
5 star 25%
4 star 50%
3 star 25%
2 star 0%
1 star 0%
Jeffrey DeLong Mar 10, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am part way through this book and am learning a lot. In particular the examples are well thought out and real world. I have spent many hours going through them, as there is a lot to learn from them. I would recommend this book to anyone interested in deep learning with some experience with Java.
Amazon Verified review Amazon
Stefan Hildebrandt Jul 03, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I've started using Java for DL projects and I had the opportunity to be one of the first readers of this book. A key insight for me was, that there are dozens of valuable examples and, considering that Java is usually not the first choice in this field, it's extremely helpful to have such a resource.
Amazon Verified review Amazon
大村 Jul 19, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Deep Learning for Java (DL4J) を紹介している点では、啓発的な本です。ただ難点は、サンプルコードが少ない 点です。DL4J の環境設定や 使用例(サンプルコード)が あれば、良かったです。
Amazon Verified review Amazon
Amazon Customer Jan 09, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I was hoping for much from this book but it is littered with spelling mistakes and throwaway confusing lines because no definition of what is being discussed is given, or is given paragraphs later. Some diagrams also do not make sense. It became increasingly irritating. This book needs a significant revision.
Amazon Verified review Amazon
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