Java Deep Learning Projects
Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
Published on 23. September 2024
436 pages
978-1-78899-652-5 (ISBN)
Description
Build and deploy powerful neural network models using the latest Java deep learning libraries
Key Features
[*] 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
Book DescriptionJava 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.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
Who this book is forIf 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.
Key Features
[*] 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
Book DescriptionJava 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.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
Who this book is forIf 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.
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
28,05 MB
ISBN-13
978-1-78899-652-5 (9781788996525)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Md. Rezaul Karim
Java Deep Learning Projects
Book
06/2018
Packt Publishing
€64.20
Shipment within 3-4 weeks
Person
Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI).
Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
Content
Table of Contents
Getting Started with Deep Learning
Cancer Type Prediction using Recurrent Type Networks
Image Classification using Convolutional Neural Networks
Sentiment Analysis using Word2Vec and LSTM Networks
Image Classification using Transfer Learning
Real-Time Object Detection Using YOLO, JavaCV, and DL4J
Stock Price Prediction Using the LSTM Network
Distributed Deep Learning
Using Deep Reinforcement Learning for a GridWorld Game
Movie Recommendation System using Factorization Machines
Discussion, Current Trends, and Outlook
Getting Started with Deep Learning
Cancer Type Prediction using Recurrent Type Networks
Image Classification using Convolutional Neural Networks
Sentiment Analysis using Word2Vec and LSTM Networks
Image Classification using Transfer Learning
Real-Time Object Detection Using YOLO, JavaCV, and DL4J
Stock Price Prediction Using the LSTM Network
Distributed Deep Learning
Using Deep Reinforcement Learning for a GridWorld Game
Movie Recommendation System using Factorization Machines
Discussion, Current Trends, and Outlook
