FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title: Artificial Intelligence for Big Data
- Author(s) Anand Deshpande , Manish Kumar
- Publisher: Packt Publishing (May 22, 2018); eBook (Free Edition)
- Permission: Free eBook by the Publisher (Packt)
- Paperback: 482 pages
- eBook HTML
- Language: English
- ISBN-10/ASIN: 1788628845/B07DGKXDLK
- ISBN-13: 978-1788628846
- Share This:
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 this book, 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.
- 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
This book is for data scientists, big data professionals, or novices who have 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.
About the Authors- N/A
-
The Promise and Peril of Big Data (David Bollier)
This book explores the positive aspects and the social perils that arise when the ever-rising floods of data being generated by mobile networking, cloud computing and other new technologies meets continued innovations in advanced correlation techniques.
-
Engineering Agile Big-Data Systems (Kevin Feeney, et al)
This book outlines an approach to dealing with problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals.
-
Knowledge Graphs and Big Data Processing (Valentina Janev, et al)
Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.
-
Big Data in Context: Legal, Social and Technological Insights
This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.
-
Modelling and Simulation for Big Data Applications
Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations.
-
O'Reilly® Big Data Now: Current Perspectives from O'Reilly Radar
This book represents report recaps the trends, tools, applications, and forecasts. This collection of blog posts, authored by leading thinkers and experts in the field, reflects a unique set of themes we've identified as gaining significant attention and traction.
-
O'Reilly® Planning for Big Data: Changing Data Landscape
This book provides an efficient, user-friendly 'brief' on the current status of Big Data analytics and how you can economically deploy this technology to increase your firm's profitability.
:
|
|