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Название: Dissecting Computer Architecture
Автор: Alvin Albuero De Luna
Издательство: Arcler Press
Год: 2023
Страниц: 272
Язык: английский
Формат: pdf (true)
Размер: 23.0 MB

This book explains the fundamental technologies and components used in modern processors and computer architectures and how varied architectural choices culminate in computer configurations that are optimal for certain applications. To put it nicely, modern computers are sophisticated gadgets. Nevertheless, when examined hierarchically, the roles of each degree of complexity become apparent. We will cover a large number of subjects in this book and will only be able to investigate each of them to a limited extent due to space constraints. Our objective is to present a comprehensive overview of each significant technology and subsystem found in a modern digital device and to explain how they interact with other system components. Professionals in every computing field must be able to grasp both hardware and software in today’s world. The interaction of hardware and software at various levels also provides a foundation for comprehending computer fundamentals. The essential ideas in computer organization and design are the same whether you’re interested in electrical engineering or computer science. As a result, the focus of this book is on demonstrating the relationship between hardware and software, as well as the concepts that underpin modern computers.

Название: Distributional Reinforcement Learning
Автор: Marc G. Bellemare, Will Dabney, Mark Rowland
Издательство: The MIT Press
Серия: Adaptive Computation and Machine Learning
Год: 2023
Страниц: 379
Язык: английский
Формат: epub (true)
Размер: 13.4 MB

The first comprehensive guide to Distributional Reinforcement Learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional Reinforcement Learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. How is distributional reinforcement learning different? In reinforcement learning, the value function describes the expected return that one would counterfactually obtain from beginning in any given state. It is reasonable to say that its fundamental object of interest – the expected return – is a scalar and that algorithms that operate on value functions operate on collections of scalars (one per state). On the other hand, the fundamental object of distributional reinforcement learning is a probability distribution over returns: the return distribution. The return distribution characterizes the probability of different returns that can be obtained as an agent interacts with its environment from a given state. Distributional reinforcement learning algorithms operate on collections of probability distributions that we call return-distribution functions (or simply return functions).

Название: Бездна. Том 1
Автор: Антон Панарин
Издательство: СИ
Год: 2023
Формат: fb2, rtf
Размер: 10 Мб
Качество: Хорошее
Язык: Русский

Я жил себе тихо-мирно, играл в теннис. Но потом припёрлась Бездна со своей Системой, разломами и зомбаками! Испоганили мне победу в турнире, да и меня едва не сожрали… За пару минут от человечества осталась лишь горстка выживших, остальные превратились в голодных тварей.
Придётся существовать в этом аду.
И выяснять, какого хрена тут творится...