Java Deep Learning Projects
Idioma: inglés
Editorial: Packt Publishing Limited, GB, 2023
- Tapa blanda
- Nuevo
Librería: Rarewaves.com USA, London, London, Reino UnidoRarewaves.com USA
Vendedor de AbeBooks desde 11 de junio de 2025
Condición: Nuevo
EUR 69,25
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Build and deploy powerful neural network models using the latest Java deep learning librariesAbout This Book. 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 processingWho 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.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 FAQsIn DetailJava 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.Style and approachA unique, learn-as-you-do approach, as the reader builds on his understanding of deep learning with Java progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.…
N° de ref. del artículo LU-9781788997454
- Título
- Java Deep Learning Projects
- Autor
- Md. Rezaul Karim
- Editorial
- Packt Publishing Limited, GB
- Año de publicación
- 2023
- Estado
- New
- Encuadernación
- Digital
- Idioma
- inglés
- ISBN 10
- 178899745X
- ISBN 13
- 9781788997454
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 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.
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 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.
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 – Video Classification Using Convolutional-LSTM Networks
- Using Deep Reinforcement Learning for a GridWorld Game
- Movie Recommendation System using Factorization Machines
- Discussion, Current Trends, and Outlook
“Sinopsis” puede pertenecer a otra edición de este título.
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Rarewaves.com USA
London, London, Reino Unido
Vendedor de AbeBooks desde 11 de junio de 2025
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