In 2003 poker was put on television and no-limit hold em quickly became the most popular form of poker played in casinos, public cardrooms, and on the Internet. At first, because of the newness of the game to most participants, they could be easily be beaten by players with only a moderate knowledge of sophisticated strategy. But today, this is no longer the case. Even though n...(展开全部)

No limit hold em is exploding in popularity. Before 2000, it could be difficult to find a game. In 2006, it is played everywhere in casino cardrooms, in bar backrooms and homes, and on the Internet. Now anyone can find a game, but few know how to play well. Most players learn by watching television or by listening to dubious advice from their friends. While they may have picke...(展开全部)

丹•哈林顿，徳州扑克大师，扑克名人堂成员，世界公认的伟大牌手之一，牌风稳健高效，被戏称为“行动丹”。他赢得的无限注徳州扑克锦标赛冠军主要包括：欧洲扑克冠军赛、世界扑克系列赛之2500美元买入无限注德州扑克比赛、四皇后无限注德州扑克冠军赛等。 比尔•罗伯特，国际象棋大师，多次国际象棋锦标赛冠军获得者，全美快棋大赛冠军；双陆棋世界顶尖高手之一，曾获得蒙特卡洛世界冠军赛、波士顿冠军赛、拉斯维加斯锦标赛等比赛冠军。比尔•罗伯特过去一直从事国际象棋和双陆棋的竞技和写作，现转为德州扑克。

Harrington on Hold ?Em takes you to the part of the game the cameras ignore ? the tactics required to get through the hundreds and sometimes thousands of hands you must win to make it to the final table. Harrington's sophisticated and time-tested winning strategies, focusing on what it takes to survive the early and middle stages of a No-Limit Hold ?Em tournament, are appearing...(展开全部)

Poker has taken America by storm. But it's not just any form of poker that has people across the country so excited it's no-limit hold 'em the main event game. And now, thanks to televised tournaments, tens of thousands of new players are eager to claim their share of poker glory. In the first volume of this series, Harrington on Hold 'em: Volume I: Strategic Play, Dan Harri...(展开全部)

Kill Everyone took the poker world by storm when it was first released in 2007. Its perfect blend of real-time experience, poker math, and computational horsepower created new concepts and advanced strategies never before seen in print for multi-table tournaments, Sit-n-Gos, and satellites. In this revised and expanded second edition, Kill Everyone adds even more ammunition to ...(展开全部)

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.