Go Deep

It was all supposed to be so simple. Navaya Howard is an erotic writer in a rut. Her readers are fed up of her stale plots and Navaya can't blame them. She's been celibate for over a year and a half since finding her now ex-boyfriend's side chick's positive pregnancy test on her bathroom counter. How can she write steamy romances if she can barely remember which body parts go into the other? Navaya enlists the help of her best friend, Xander, to revive the inspiration that used to have her sitting comfortably at the top of her game. What happens when the sex hits deeper than either of them expected and the tender emotions can no longer be denied? Navaya and Xander's arrangement has gone far deeper than intended. Will their friendship and their hearts survive the fall? --Back cover.

So you're involved in youth ministry. How do you lead? How do you know yourself and keep yourself connected? What personal practices do you embrace? How do you optimize your time with youth so that they (and you) may come to know God more fully? What resources might you use for practical help? Where would you find these ideas? Go Deep! Open Doris Kizinna's book and read about how to prepare and ground yourself, as youth leader, in your personal spiritual practice. Then, using practices drawn from many traditions and designed to open hearts and minds to an all-encompassing wonder of the Holy One, engage and mentor youth on the path of Christian spirituality. Go Deep offers inspiration and practical tools to youth leaders who want to develop and deepen their youth ministry, both for themselves and for the youth they minister to. The book is divided into two sections: Part 1 addresses the leader and Part 2 describes the various spiritual practices. Part 2 also includes prayer practices: to engage the senses of written and spoken word of creativity of movement and body of compassion of community of worship and song

In the winter of 1993, Don Neworth, international dead-beat, and Ruth Cohen, infamous double agent, are having a stand-off on the snow and ice-covered stairs that lead up/down the famous HolmenKollen Ski Jump in Oslo, Normandy. They are not there to sight-see. She wears gloves. Don doesn’t. To Don, that’s not fair. Ruth is an undercover Hamas operator passing herself off as Mossad, the Israeli FBI. Don is a newshound who wants the big story so he can return to the USA in an explosion of literary fame instead of being a guy who dodged the Vietnam draft, drinks too much, and is always, always on the make. They met in Frankfort where Don worked for Stars & Stripes, Europe. He was covering an anti-Semitic incident involving him and she was posing as a member of the Mossad. Ruth’s extraordinarily beauty mesmerized Don into forgetting his quest for the big story. Maybe, he should have guessed something was wrong after the third time she drugged him and set him up ala Lee Harvey Oswald, “Deranged American Newsman Blows Up Oslo Accords.” He’d take the blame for the explosion that was designed to wipe out the Arab and Jewish peace negotiators. Don, in spite of his avowed cowardice, reluctantly tries to save their lives by driving them in a beat-up taxi with a blown-out windshield, through a snowstorm, dodging Uzi bullets, playing bumper tag at high speeds, and hiding in the famous Vigeland Park. All of which leads to the two antagonists’ rendezvous on the icy ski jump stairs. Really, it would only be fair it he had gloves too. Maybe he can take hers after she’s dead.

Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

‘One of the boldest thriller writers working today’ TESS GERRITSEN ‘Her characters, plot, and pacing are unrivalled’ MICHAEL CONNELLY __________________________________ A hallucinatory noir short story from the No.1 bestselling author of the Will Trent novels The arrangement is simple. Charlie picks up a suit from the dry cleaner’s. In the suit pocket is the name of a very important man. The next day, that man walks into the dealership, drives out in a new car, and Charlie gets a fat envelope full of cash. Everyone’s happy. No one gets hurt. So long as Charlie doesn’t cross his business partner. But with one twist of a knife, the unthinkable happens. And suddenly Charlie is in deeper trouble than he could have possibly imagined... 'Go Deep' is also available as part of a bundle with 'Remmy Rothstein Toes the Line' and 'Necessary Women'

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.