Picture this, again; you walk into a grocery store, you get the groceries you want, and then walk out. No lines, no payment, just walk out. With it's sensor fusion, computer vision and deep learning technology, Amazon is able to identify you and charge your account, without you having to do anything. You scan your phone via the Amazon app, walk in, shop, and then leave. In order for this to work, I'll let you figure out what data points you have to give up.

Trigo is a provider of AI & computer vision based checkout-free systems for the retail market, enabling frictionless checkout and a range of other in-store operational and marketing solutions such as predictive inventory management, security and fraud prevention, pricing optimization and event-driven marketing. The system is based on ceiling-mounted off-the-shelf cameras and sensors, powered by proprietary deep learning algorithms, built to map and analyze the location and movement of every object throughout the store. When shoppers pick an item from the shelf, the system automatically detects the event and adds it to a virtual shopping list. Once the shoppers are done, they can simply walk out with no need to go through conventional checkout. Upon leaving the store, customers are charged automatically and the receipt is sent to their mobile device.

Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. Previously he worked as a machine learning scientist in a variety of data-driven domains and applied his ML expertise in computational advertising, marketing and cybersecurity. He is now developing and improving the machine learning models and systems for ads optimization on the largest search engine in the world. He is an author of a series of machine learning books and an education enthusiast. His first book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018, and was translated into many different languages.

The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.

This book takes you on a journey from the origins of machine learning to the latest deep learning architectures. Through conceptual and practical examples, you'll develop a repertoire of techniques that allow you to solve a wide range of predictive modeling tasks, including tabular, image, and text data. PyTorch is a very powerful and versatile tool, and deep learning naturally requires very flexible building blocks. Hence, PyTorch can sometimes be very verbose compared to traditional machine learning libraries such as scikit-learn. In this book, we explain how PyTorch works and cover all the essential parts.

Applying deep learning to audio/music and voice recognition Working with neural networks and image files Creating a stock price prediction algorithm Using artificial intelligence with Thompson sampling Using deep learning to predict crime statistics Using neural networks for binary classification Building a convolutional neural network to classify your own image files Train your computer to "read" and "understand" the English language Using SQL in neural networks Train your computer to "read" and "understand" the English language

Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.

We are living in exciting times! Some of us have been fortunate to have lived through huge advances in technology--the invention of the personal computer, the dawn of the internet, the proliferation of cell phones, and the advent of social media. And now, major breakthroughs are happening in AI! It's exciting to watch and be a part of this change. I think we're just getting started, and it's amazing to think of how the world might change over the next decade. How great it is that we're living during these times and can participate in the expansion of AI? PyTorch has, no doubt, enabled some of the finest advances in deep learning and AI.

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

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.