Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering. He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well.

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.

I thought this was a very well-written book on Deep Learning (DL). Java is (in my opinion) not the best language for teaching algorithms, but the example code is very readable. Like many DL books, the book focuses a lot on basic concepts and the math derivations behind them, so in that sense it is relatively undifferentiated from these books - however, it is is the only one that does so in Java. This is the only book I have read that has extensive coverage of pre-training (Deep Belief Networks, Restricted Boltzmann Machines, Denoising Autoencoders (DA), and Stacked DAs). Other "standard" networks such as Multilayer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks are also covered, about as well as other books I have read.

Jeff Heaton is a data scientist, PhD student and indy publisher. Specializing in Java, C#, C/C, Python and R, he is an active technology blogger, open source contributor, and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University. He is also a senior member of the IEEE, a Sun-Certified Java Programmer, the lead developer for the Encog Machine Learning Framework open source project, and a fellow of the Life Management Institute (FLMI).

Mobile has become an integral part of the shopping experience. As consumers spend more time with their mobile devices than with desktop and notebook computers combined, they're looking to their smartphones and tablets to complete more complex transactions. Yet, there is still room to grow for the retail industry's mobile experiences. While nearly one-third of retailer web traffic is from mobile devices, only 11 percent of sales come from mobile, according to Forrester's U.S. Mobile Phone and Tablet Commerce Forecast, 2015 - 2020. Mobile gives retailers opportunities to entice new customers, and retain returning customers, by allowing them to shop on their own terms.

Artificial intelligence and machine learning will create computers so sophisticated and godlike that humans will need to implant "neural laces" in their brains to keep up, Tesla Motors and SpaceX CEO Elon Musk told a crowd of tech leaders this week. While Musk's description of an injectable human-computer link may sound like science fiction, top tech executives repeatedly said that artificial intelligence (AI) was on the verge of changing everyday life, during discussion at a conference by online publication Recode this week. It is no secret that tech companies are diving into AI analytics research, an industry that will grow to $70 billion by 2020 from just $8.2 billion in 2013, according to a Bank of America report citing IDC research. AI, which combs through large troves of raw data to predict outcomes and recognize patterns, is already used in web search systems, marketing recommendation functions and security and financial trading programs. The technology will spread to driverless cars and service robots in the future, the Bank of America report said.

Most people have an Amazon Echo in their kitchen or living room - but the CEO of Amazon keeps one in his bathroom. In a recent interview with Billboard, Jeff Bezos revealed there is a smart speaker in every room of his house, including his water closet. The tech tycoon moved a device into the room after being frustrated when he could not ask Alexa about the weather while on the toilet. Most people keep an Amazon Echo in their kitchen or living room, but the CEO of Amazon has one in his bathroom. Amazon Echo is a voice-controlled smart speaker that works alongside a smartphone app.

The use of artificial intelligence in retail settings remains a vague and experimental concept, but early results from lingerie chain Cosabella suggests that even early uses of automation can drive online-to-offline sales. Emarsys artificial intelligence (AI) enabled marketing automation platform to enhance customer engagement and acquisition, and conversion growth. The Italian-based chain, which operates boutiques in New York and Florida in addition to selling its brand at Nordstrom department stores, began using an AI platform operated by mobile tech provider Emarsys in October 2016. The specific tools Cosabella has been using include Emarsys's Automation, Predict Web Extend, Smart Insight and CRM Ads products. Since the integration of that platform, Cosabella says it has seen a doubled its email subscriber list.

According to analyst Forrester, AI, big data and analytics will increase businesses' access to data, broaden the types of data that can be analysed, and raise the level of sophistication of the resulting insight. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.

Mobile has become an integral part of the shopping experience. As consumers spend more time with their mobile devices than with desktop and notebook computers combined, they're looking to their smartphones and tablets to complete more complex transactions. Yet, there is still room to grow for the retail industry's mobile experiences. While nearly one-third of retailer web traffic is from mobile devices, only 11 percent of sales come from mobile, according to Forrester's U.S. Mobile Phone and Tablet Commerce Forecast, 2015 - 2020. Mobile gives retailers opportunities to entice new customers, and retain returning customers, by allowing them to shop on their own terms.