Deep Learning: Practical Neural Networks with Java
Build and run intelligent applications by leveraging key Java machine learning libraries
Published on 8. June 2017
Book
Paperback/Softback
744 pages
978-1-78847-031-5 (ISBN)
Description
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
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
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 40 mm
Weight
1363 gr
ISBN-13
978-1-78847-031-5 (9781788470315)
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
Alan M. F. Souza | Fabio M. Soares | Yusuke Sugomori
Deep Learning: Practical Neural Networks with Java
Build and run intelligent applications by leveraging key Java machine learning libraries
E-Book
04/2025
Packt Publishing
€82.99
Available for download
Persons
Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users' respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot ofattention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015.
In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight "new generation" creators by the Japanese magazine Web Designing.
In April 2016, he joined a medical start-up as cofounder and CTO. Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight.Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. Bostjan has extensive experience in Java and Python, and he also lectures on Weka in the classroom.Focusing on machine learning and data science, Bostjan has published numerous articles in professional journals, delivered conference papers, and authored or contributed to a number of patents. In 2013, Bostjan published his first book on data science, Instant Weka How-to, Packt Publishing, exploring how to leverage machine learning using Weka. Learn more about him at http://bostjankaluza.net. Fabio M. Soares is currently a PhD candidate at the Federal University of Para (Universidade Federal do Para - UFPA), in northern Brazil. He is very passionate about technology in almost all fields, and designs neural network solutions since 2004 and has applied this technique in several fields like telecommunications, industrial process control and modeling, hydroelectric power generation, financial applications, retail customer analysis and so on. His research topics cover supervised learning for data-driven modeling. As of 2017, he is currently carrying on research projects with chemical process modeling and control in the aluminum smelting and ferronickel processing industries, and has worked as a lecturer teaching subjects involving computer programming and artificial intelligence paradigms. As an active researcher, he has also a number of articles published in English language in many conferences and journals, including four book chapters. Alan M. F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM). He holds a post-graduate degree in project management software and a master's degree in industrial processes (applied computing) from Universidade Federal do Para (UFPA). He has been working with neural networks since 2009 and has worked with Brazilian IT companies developing in Java, PHP, SQL, and other programming languages since 2006. He is passionate about programming and computational intelligence. Currently, he is a professor at Universidade da Amazonia (UNAMA) and a PhD candidate at UFPA.
In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight "new generation" creators by the Japanese magazine Web Designing.
In April 2016, he joined a medical start-up as cofounder and CTO. Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight.Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. Bostjan has extensive experience in Java and Python, and he also lectures on Weka in the classroom.Focusing on machine learning and data science, Bostjan has published numerous articles in professional journals, delivered conference papers, and authored or contributed to a number of patents. In 2013, Bostjan published his first book on data science, Instant Weka How-to, Packt Publishing, exploring how to leverage machine learning using Weka. Learn more about him at http://bostjankaluza.net. Fabio M. Soares is currently a PhD candidate at the Federal University of Para (Universidade Federal do Para - UFPA), in northern Brazil. He is very passionate about technology in almost all fields, and designs neural network solutions since 2004 and has applied this technique in several fields like telecommunications, industrial process control and modeling, hydroelectric power generation, financial applications, retail customer analysis and so on. His research topics cover supervised learning for data-driven modeling. As of 2017, he is currently carrying on research projects with chemical process modeling and control in the aluminum smelting and ferronickel processing industries, and has worked as a lecturer teaching subjects involving computer programming and artificial intelligence paradigms. As an active researcher, he has also a number of articles published in English language in many conferences and journals, including four book chapters. Alan M. F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM). He holds a post-graduate degree in project management software and a master's degree in industrial processes (applied computing) from Universidade Federal do Para (UFPA). He has been working with neural networks since 2009 and has worked with Brazilian IT companies developing in Java, PHP, SQL, and other programming languages since 2006. He is passionate about programming and computational intelligence. Currently, he is a professor at Universidade da Amazonia (UNAMA) and a PhD candidate at UFPA.