Presents the story behind the self-learning artificial intelligence system with its stunning chess skills

I should not exist.All children like me are stillborn, or die in infancy. Those who cannot grow stronger, die. No empty child has ever reached a year of age, yet I am now thirteen.It has been a long and miserable thirteen years, where the best I can manage to do is walk with difficulty. Sometimes, I cannot even manage that.My clan has paid dearly for every minute of my life. And money is not so easy to obtain, here at the edge of civilization.Perhaps I might have lived in this state for many years. A cripple, strong in mind but feeble in body. But when some unexpected guests came to our estate, everything changed. I would die at last - or, I would learn to survive on my own.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

During the great apocalypse, humanity fled to the depths of the underground enclaves. In genetic laboratories, researchers tried to breed a new being, splicing human and animal DNA, creating a beast intelligent yet strong enough to survive in the devastated world. The enclaves have fallen - but the animals fight for freedom has only just begun.

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.

A NEW YORK TIMES NOTABLE BOOK The Babylonians invented it, the Greeks banned it, the Hindus worshipped it, and the Christian Church used it to fend off heretics. Today it's a timebomb ticking in the heart of astrophysics. For zero, infinity's twin, is not like other numbers. It is both nothing and everything. Zero has pitted East against West and faith against reason, and its intransigence persists in the dark core of a black hole and the brilliant flash of the Big Bang. Today, zero lies at the heart of one of the biggest scientific controversies of all time: the quest for a theory of everything. Within the concept of zero lies a philosophical and scientific history of humanity. Charles Seife's elegant and witty account takes us from Aristotle to superstring theory by way of Egyptian geometry, Kabbalism, Einstein, the Chandrasekhar limit and Stephen Hawking. Covering centuries of thought, it is a concise tour of a world of ideas, bound up in the simple notion of nothing.

A novel on the political madness of our time and the Internet’s deep workings, by the author of The Infernal One year after the president has plunged the world into nuclear war, a journalist takes refuge in the Twin Cities Metro Containment Zone. On assignment, she documents internet humor at the end of the world, hoping along the way to find the final resting place of her wife and daughter. What she uncovers, hidden amid spiraling memes and twitter jokes in an archive of the internet’s remnants, are references to an enigmatic figure known only as Birdcrash, who may hold the key to an uncertain future.

Deep Neural Networks have revolutionized computer engines for Go, Shogi and chess. Finally computers are able to evaluate a game position similiar to the way human experts do it. By that, computers are able to identify long-term strategic advantages and disadvantages. But how do chess engines based on neural networks such as AlphaZero, Leela Chess Zero actually work? This book gives an answer to that question. With lots of practical examples and illustrations, all basic building blocks that are required to understand modern chess are introduced. Based on that, the concepts of both classic and modern chess engines are explained. Finally, a miniature version of AlphaZero to play the game Hexapawn is implemented in Python. Chapters include: Single-Layer and Multilayer Perceptrons, Back-Propagation and Gradient Descent, Classification and Regression, Network Vectorization, Convolutional Layers, Squeeze and Excitation Networks, Fully Connected Layers, Batch Normalization, Rectified Linear Unit (ReLU), Residual Layers, Minimax, Alpha-Beta Search, Monte-Carlo Tree Search, AlphaGo, AlphaGo Zero, AlphaZero, Leela Chess Zero (Lc0), Fat Fritz, Effectively Updateable Neural Networks, Fat Fritz 2, Maia, Supervised Learning Hexapawn, Reinforcement Learning of Hexapawn (Hexapawn Zero)

Jose Raul Capablanca is renowned for his exquisite positional play and flawless endgame technique. But The Chess Machine was also a master of that other way to deliver mate: the attack on the enemy king.In this groundbreaking work, award-winning chess coach and author Frisco Del Rosario shines a long-overdue light on this neglected aspect of Capablanca's record. He illustrates how the Cuban genius used positional concepts to build up irresistible king hunts, embodying the principles of good play advocated by the unequaled teacher, C.J.S. Purdy. The author also identifies an overlooked checkmate pattern - Capablanca's Mate - that aspiring attackers can add to the standard catalogue in Renaud and Kahn's The Art of the Checkmate. As Del Rosario shows, Capablanca has inspired not only generations of players, but also many of the classics of chess literature.Easy to read but chock-full of advice for study and practical play, Capablanca: A Primer of Checkmate fills a gaping hole in our understanding of the third World Champion.

Contact made with an extraterrestrial known as Alpha Beta 777 Zero and his message for the earthlings.