Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens to your business when you're no longer bound by language? It's common today to have virtual meetings with international teams and customers that speak many different languages. Whether they're internal or external meetings, meaning often gets lost in complex discussions and you may encounter language barriers that prevent you from being as effective as you could be.

New commerce models are starting to emerge as we head into the future of the Metaverse. Look at any Target or Walmart store on a Saturday and watch as customers perfectly dominate the essence of physical-to-physical commerce. In fact, just the experience of being in a physical location leads most customers to make purchases far beyond their shopping lists. That's the reason why brands spend millions of dollars on physical retail locations because they feel confident they can elevate and capitalize on the on-site shopping experience and the "serendipity" that happens in the store. Whether it's waiting in a queue to enter the Louis Vuitton Maison Vendôme store in Paris or going down an in-store slide during a Showfields shopping adventure in New York, the world of physical retail has become more experiential and glitzy. It is one of the drivers of BIG retail.

Michael Beyeler is an Assistant Professor at the University of California, Santa Barbara, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis ("bionic eye"). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. Michael is the author of four programming books focusing on computer vision and machine learning. He is also an active contributor to several open-source software projects, and has professional programming experience in Python, C/C, CUDA, MATLAB, and Android. Michael received a Ph.D. in Computer Science from the University of California, Irvine as well as a M.Sc. in Biomedical Engineering and a B.Sc. in Electrical Engineering from ETH Zurich, Switzerland.

Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data.

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.