Artificial Intelligence for Big Data (eBook)

Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Anand Deshpande, Manish Kumar (Autoren)

eBook Download: EPUB

2018
384 Seiten
Packt Publishing (Verlag)
978-1-78847-601-0 (ISBN)

Infos

Build next-generation Artificial Intelligence systems with Java

Key Features

• Implement AI techniques to build smart applications using Deeplearning4j
• Perform big data analytics to derive quality insights using Spark MLlib
• Create self-learning systems using neural networks, NLP, and reinforcement learning

Book Description

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.

With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.

By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.

What you will learn

• Manage Artificial Intelligence techniques for big data with Java
• Build smart systems to analyze data for enhanced customer experience
• Learn to use Artificial Intelligence frameworks for big data
• Understand complex problems with algorithms and Neuro-Fuzzy systems
• Design stratagems to leverage data using Machine Learning process
• Apply Deep Learning techniques to prepare data for modeling
• Construct models that learn from data using open source tools
• Analyze big data problems using scalable Machine Learning algorithms

Who this book is for

This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

Anand Deshpande is the Director of big data delivery at Datametica Solutions. He is responsible for partnering with clients on their data strategies and helps them become data-driven. He has extensive experience with big data ecosystem technologies. He has developed a special interest in data science, cognitive intelligence, and an algorithmic approach to data management and analytics. He is a regular speaker on data science and big data at various events. Manish Kumar is a Senior Technical Architect at Datametica Solutions. He has more than 11 years of industry experience in data management as a data, solutions, and product architect. He has extensive experience in building effective ETL pipelines, implementing security over Hadoop, implementing real-time data analytics solutions, and providing innovative and best possible solutions to data science problems. He is a regular speaker on big data and data science.

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Build next-generation Artificial Intelligence systems with JavaAbout This BookImplement AI techniques to build smart applications using Deeplearning4jPerform big data analytics to derive quality insights using Spark MLlibCreate self-learning systems using neural networks, NLP, and reinforcement learningWho This Book Is ForThis book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.What You Will LearnManage Artificial Intelligence techniques for big data with JavaBuild smart systems to analyze data for enhanced customer experienceLearn to use Artificial Intelligence frameworks for big dataUnderstand complex problems with algorithms and Neuro-Fuzzy systemsDesign stratagems to leverage data using Machine Learning processApply Deep Learning techniques to prepare data for modelingConstruct models that learn from data using open source toolsAnalyze big data problems using scalable Machine Learning algorithmsIn DetailIn this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.Style and approachAn easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Produkt-Details

• Erscheint lt. Verlag: 22.5.2018
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: AI • Artificial Intelligence • Big Data • Clustering • deeplearning4j • Genetic algorithms • machine learning • MongoDB • Neural networks • NoSQL • RBF • Regression techniques • Spark MLlib • SVM • Swarm intelligence
• ISBN-10: 1-78847-601-8 / 1788476018
• ISBN-13: 978-1-78847-601-0 / 9781788476010