With data and algorithms, companies in the sector can put themselves in the shoes of their consumers and always manage to offer them the products and/or services that best suit each profile. Also, with the development of production processes, investments are made to improve products and offer greater variety. In this way, consumers benefit from more refined and select products. AI makes it possible for companies to be more adaptable to each of their consumers, a personalization that is so crucial in today's customer demands. In recent years, data science has experienced a boom thanks to the number of insights it provides, not only about customers but also about the processes themselves.

Zbigniew Michalewicz is Emeritus Professor of Computer Science at the University of Adelaide in Australia. He completed his Masters degree at Technical University of Warsaw in 1974 and he received Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, in 1981. He also holds a Doctor of Science degree in Computer Science from the Polish Academy of Science. Zbigniew Michalewicz also holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, the Polish-Japanese Institute of Information Technology, and the State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with the Structural Complexity Laboratory at Seoul National University, South Korea.

Where one retailer goes, others love to follow. What does that mean for you during Amazon's Prime Day sale? Expect Walmart Prime Day deals on TVs, laptops, robot vacuums, headphones, tablets, and more on July 12 and 13. In fact, expect to see discounts roll out before the start of Amazon Prime Day. Historically, Walmart likes to start early and go longer.

Lior Rokach is a computer scientist. He is a professor and the former chair of the Department of Software and Information Systems Engineering (SISE) at Ben-Gurion University of the Negev (BGU). Lior was born in 1972 in Holon, Israel. He completed his B.Sc., M.Sc., and Ph.D. in 1998,1999, and 2004 respectively at Tel-Aviv University. His research interests lie in designing and analyzing Machine Learning and Data Mining algorithms and their applications in Recommender Systems, Cyber Security, and Medical Informatics.

The TensorFlow ecosystem has evolved into many different frameworks to serve a variety of roles and functions. That flexibility is part of the reason for its widespread adoption, but it also complicates the learning curve for data scientists, machine learning (ML) engineers, and other technical stakeholders. There are so many ways to manage TensorFlow models for common tasks--such as data and feature engineering, data ingestions, model selection, training patterns, cross validation against overfitting, and deployment strategies--that the choices can be overwhelming. This pocket reference will help you make choices about how to do your work with TensorFlow, including how to set up common data science and ML workflows using TensorFlow 2.0 design patterns in Python. Examples describe and demonstrate TensorFlow coding patterns and other tasks you are likely to encounter frequently in the course of your ML project work.

Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.

Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI's turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us?

Imran has been a part of cutting-edge research about Algorithms and Machine Learning for the last many years. He completed his PhD in 2010 in which he proposed a new Linear Programming based algorithm which can be used to optimally assign resources in a large scale cloud computing environment. In 2017, Imran developed a realtime analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various Machine Learning Algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at Canadian Federal Government as a Data Scientist where he is using Machine Learning Algorithms for critical use-cases. Imran is a visiting professor at Carleton University, Ottawa. Imran has also been teaching for Google and Learning Tree for the last many years. The topics Imran teaches include Algorithms, Cloud Computing and Deep Learning. Over his career, Imran has written many research papers and a couple of his recent papers have won the best paper award. Imran also regularly writes blogs on selected IT topics. In addition to his professional work, Imran is into Nature Photography. Over the years he has taken thousands of photos about nature. Imran's passion is to find a way to make technology work for the betterment of humanity. This passion is the main motivation behind his research.

Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own.

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence ranging from Natural Language Processing(NLP), Computer Vision to Generative Modeling using GANs. He co-founded Mate Labs and currently leading the technical team of Mate Labs. He uses GANs for building different models such as turning painting into photos and enhancing the resolution of images etc. He is super optimistic about Artificial General Intelligence and believes that AI is going to be the workhorse of human evolution.