Prof. Jong Chul Ye is a Professor of the Graduate School of AI and Affiliated Professor at Dept. of Bio/Brain Engineering and Dept. of Mathematical Sciences of Korea Advanced Institute of Science and Technology (KAIST), Korea. Before joining KAIST, he was a postdoctoral fellow at the University of Illinois at Urbana Champaign, a Senior Researcher at Philips Research at New York, and then GE Global Research in Niskayauna. He has served as an associate editor of IEEE Trans. He is currently an associate editor for IEEE Trans. He is an IEEE Fellow, and was the Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer in 2021-2022.

Tadej Magajna is a former lead machine learning engineer, former data scientist and now a software engineer at Microsoft. He currently works in a team responsible for language model training and building language packs for keyboards such as Microsoft SwiftKey. He is also a Master of computer science. He started his career as a 15-year-old at a local media company as a web developer and progressed towards more complex engineering and machine learning problems. He tackled problems like NLP market research, public transport bus and train capacity forecasting and finally language model training at his current role.

Building a recommendation engine Evaluating recommender systems Content-based filtering using item attributes Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF Model-based methods including matrix factorization and SVD Applying deep learning, AI, and artificial neural networks to recommendations Session-based recommendations with recursive neural networks Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines Using the Tensorflow Recommenders Framework (TFRS) to develop and deploy deep learning-based recommender systems Using SaaS platforms such as Amazon Personalize, Recombee, and RichRelevance Using Generative Adversarial Networks (GAN's) to generate user recommendations Real-world challenges and solutions with recommender systems Case studies from YouTube and Netflix Building hybrid, ensemble recommenders Using Generative Adversarial Networks (GAN's) to generate user recommendations

Let's say you have identified a use case in your organization that you would like to handle via a chatbot. You familiarized yourself with Amazon Lex, built a prototype, and did a few trial interactions with the bot. You liked the overall experience and now want to deploy the bot in your production environment, but aren't sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations. We have covered the planning, design, and configuration phases in previous blog posts.

Ashish Ranjan Jha received his Bachelors degree in Electrical Engineering from IIT Roorkee (India), Masters degree in Computer Science from EPFL (Switzerland) and an MBA degree from Quantic School of Business (Washington). He has received distinction in all 3 of his degrees. He has worked for large technology companies like Oracle, Sony as well as the more recent tech unicorns such as Revolut, mostly focussed around Artificial Intelligence. He currently works as a Machine Learning Engineer. Ashish has several years of working experience and specialisation in the field of Machine Learning, and Python is his go-to tool.

This book is not an introduction to ML. There are many books, courses, and resources available for ML theories, and therefore, this book shies away from these concepts to focus on the practical aspects of ML. You don't have to know these topics inside out--for concepts whose exact definitions can take some effort to remember, e.g., F1 score, we include short notes as references--but you should have a rough sense of what they mean going in. While this book mentions current tools to illustrate certain concepts and solutions, it's not a tutorial book. Tools go in and out of style quickly, but fundamental approaches to problem solving should last a bit longer.

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.