Einstein said that the most incredible thing about our universe was that it was comprehensible at all. As Kitty Ferguson explains, Pythagoras had much the same idea - but 2,500 years earlier. Though known by many only for his famous Theorem, in fact the pillars of our scientific tradition - belief that the universe is rational, that there is unity to all things, and that numbers and mathematics are a powerful guide to truth about nature and the cosmos - hark back to the convictions of this legendary scholar. Kitty Ferguson brilliantly evokes Pythagoras' ancient world of, showing how ideas spread in antiquity, and chronicles the incredible influence he and his followers have had on so many extraordinary people in the history of Western thought and science. 'Pythagoras' influence on the ideas, and therefore on the destiny, of the human race was probably greater than that of any single man before or after him' - Arthur Koestler.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    A modern classic by an accomplished mathematician and best-selling author has been updated to encompass and explain the recent headline-making advances in the field in non-technical terms.

    Until he didn't anymore. Blindsided by the loss of his mother and his marriage in the same month, Millburn started questioning every aspect of the life he had built for himself. Then, he accidentally discovered a lifestyle known as minimalism...and everything started to change. That was four years ago. Since, Millburn, now 32, has embraced simplicity. In the pursuit of looking for something more substantial than compulsory consumption and the broken American Dream, he jettisoned most of his material possessions, paid off loads of crippling debt, and walked away from his six-figure career. So, when everything was gone, what was left? Not a how-to book but a why-to book, Everything That Remains is the touching, surprising story of what happened when one young man decided to let go of everything and begin living more deliberately. Heartrending, uplifting, and deeply personal, this engrossing memoir is peppered with insightful (and often hilarious) interruptions by Ryan Nicodemus, Millburn's best friend of twenty years.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Game Theory is the mathematical formalization of interactive decision-making - it assumes that each player's goal is to maximize his/her benefit, whatever it may be. Players may be friends, foes, political parties, states, or any entity that behaves interactively, whether collectively or individually. One of the problems with game analysis is the fact that, as a player, it's very hard to know what would benefit each of the other players; some of us are not even clear about our own goals or what might actually benefit us. Haim Shapira uses multiple examples to explain what Game Theory is and how the different interactions between decision-makers can play out. In this book you will: Meet the Nobel Laureate John F Nash and familiarize yourself with his celebrated equilibrium Learn the basic ideas of the art of negotiation Visit the gladiators' ring and apply for a coaching position Build an airport and divide inheritance Issue ultimatums and learn to trust

    Roosevelt expresses her philosophy of life by relating the experiences which have enabled her to cope with personal and public responsibilities.

    Thrown into a profession of gallows humor and vivid characters (both living and very dead), Caitlin learned to navigate the secretive culture of those who care for the deceased.Smoke Gets in Your Eyes tells an unusual coming-of-age story full of bizarre encounters and unforgettable scenes. Caring for dead bodies of every color, shape, and affliction, Caitlin soon becomes an intrepid explorer in the world of the dead. She describes how she swept ashes from the machines (and sometimes onto her clothes) and reveals the strange history of cremation and undertaking, marveling at bizarre and wonderful funeral practices from different cultures.Her eye-opening, candid, and often hilarious story is like going on a journey with your bravest friend to the cemetery at midnight. She demystifies death, leading us behind the black curtain of her unique profession. And she answers questions you didn’t know you had: Can you catch a disease from a corpse? How many dead bodies can you fit in a Dodge van? What exactly does a flaming skull look like?Honest and heartfelt, self-deprecating and ironic, Caitlin's engaging style makes this otherwise taboo topic both approachable and engrossing. Now a licensed mortician with an alternative funeral practice, Caitlin argues that our fear of dying warps our culture and society, and she calls for better ways of dealing with death (and our dead).

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    "A stunning memoir from the astronaut who spent a record-breaking year aboard the International Space Station – a candid account of his remarkable voyage, the journeys that preceded it, and his colorful formative years." Inside book cover comments.

    In Peter Woit's view, superstring theory is just such an idea. In Not Even Wrong , he shows that what many physicists call superstring "theory" is not a theory at all. It makes no predictions, even wrong ones, and this very lack of falsifiability is what has allowed the subject to survive and flourish. Not Even Wrong explains why the mathematical conditions for progress in physics are entirely absent from superstring theory today and shows that judgments about scientific statements, which should be based on the logical consistency of argument and experimental evidence, are instead based on the eminence of those claiming to know the truth. In the face of many books from enthusiasts for string theory, this book presents the other side of the story.

    Riz is the Executive Director of Play Labs @ MIT, a video game startup incubator at the MIT Game Lab.Drawing from research and concepts from computer science, artificial intelligence, video games, quantum physics, and referencing both speculative fiction and ancient eastern spiritual texts, Virk shows how all of these traditions come together to point to the idea that we may be inside a simulated reality like the Matrix.The Simulation Hypothesis is the idea that our physical reality, far from being a solid physical universe, is part of an increasingly sophisticated video game-like simulation, where we all have multiple lives, consisting of pixels with its own internal clock run by some giant Artificial Intelligence. Simulation theory explains some of the biggest mysteries of quantum and relativistic physics, such as quantum indeterminacy, parallel universes, and the integral nature of the speed of light.Recently, the idea that we may be living in a giant video game has received a lot of attention:"There's a one in a billion chance we are not living in a simulation" -Elon Musk"I find it hard to argue we are not in a simulation." -Neil deGrasse Tyson"We are living in computer generated reality." -Philip K. DickVideo game technology has developed from basic arcade and text adventures to MMORPGs. Video game designer Riz Virk shows how these games may continue to evolve in the future, including virtual reality, augmented reality, Artificial Intelligence, and quantum computing. This book shows how this evolution could lead us to the point of being able to develop all encompassing virtual worlds like the Oasis in Ready Player One, or the simulated reality in the Matrix.While the idea sounds like science fiction, many scientists, engineers, and professors have given the Simulation Hypothesis serious consideration. Futurist Ray Kurzweil has popularized the idea of downloading our consciousness into a silicon based device, which would mean we are just digital information after all. Some, like Oxford lecturer Nick Bostrom, goes further and thinks we may in fact be artificially intelligent consciousness inside such a simulation already!But the Simulation Hypothesis is not just a modern idea. Philosophers like Plato have been telling us that we live in a "cave" and can only see shadows of the real world. Mystics of all traditions have long contended that we are living in some kind of "illusion "and that there are other realities which we can access with our minds. While even Judeo-Christian traditions have this idea, Eastern traditions like Buddhism and Hinduism make this idea part of their core tradition -- that we are inside a dream world ("Maya" or illusion, or Vishnu's Dream), and we have "multiple lives" playing different characters when one dies, continuing to gain experience and "level up" after completing certain challenges. Sounds a lot like a video game!Whether you are a computer scientist, a fan of science fiction like the Matrix movies, a video game enthusiast, or a spiritual seeker, The Simulation Hypothesis touches on all these areas, and you will never look at the world the same way again!