彼得帕克（汤姆·赫兰德 Tom Holland 饰）在内战后受到了钢铁侠托尼斯塔克（小罗伯特·唐尼 Robert Downey Jr. 饰）的赏识，表面上，彼得进入托尼的公司成为了一名实习生，实际上，他和复仇者联盟的成员们一起接受了各种各样的训练。托尼虽然欣赏彼得的勇敢和正直，却并不认为他目前已经拥有加入复联的实力，他派出了特工哈皮（乔恩·费儒 Jon Favreau 饰）暗中观察，这让十分想证明自己的彼得感到万分焦躁。
在对付两个银行劫匪的过程中，彼得发现劫匪们使用的是一种前所未见的新型武器，他孤身一人深入敌后顺藤摸瓜找到了幕后主使秃鹰（迈克尔·基顿 Michael Keaton 饰），让他彼得没有想到的是，秃鹰竟然是他爱慕的女生利兹（劳拉·海瑞尔 Laura Harrier 饰）的父亲。

影片用一封无法投递的信件串联起梵高在去世前最后六周里与他生命中最重要的三个人物之间的秘密，带观众穿越回十九世纪末的欧洲，并陪伴梵高走完他最后的人生。影片采用梵高原画作品中的人物原型还原梵高的艺术人生，让观众在享受美得令人窒息的视觉盛宴时，抽丝剥茧地发现隐藏了一个半世纪的秘密。

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

This volume systematically details both the basic principles and new developments in Data Envelopment Analysis (DEA), offering a solid understanding of the methodology, its uses, and its potential. New material in this edition includes coverage of recent developments that have greatly extended the power and scope of DEA and have lead to new directions for research and DEA uses. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. The first nine chapters cover the basic principles of DEA, while the final seven chapters provide a more advanced treatment.

Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

This title now updated: the definitive neuroscience resource-from Eric R. Kandel, MD (winner of the Nobel Prize in 2000); James H. Schwartz, MD, PhD; Thomas M. Jessell, PhD; Steven A. Siegelbaum, PhD; and A. J. Hudspeth, PhD 900 full-color illustrations. Deciphering the link between the human brain and behavior has always been one of the most intriguing - and often challenging-aspects of scientific endeavor. The sequencing of the human genome, and advances in molecular biology, have illuminated the pathogenesis of many neurological diseases and have propelled our knowledge of how the brain controls behavior. To grasp the wider implications of these developments and gain a fundamental understanding of this dynamic, fast-moving field, Principles of Neuroscience stands alone as the most authoritative and indispensible resource of its kind. In this classic text, prominent researchers in the field expertly survey the entire spectrum of neural science, giving an up-to-date, unparalleled view of the discipline for anyone who studies brain and mind. Here, in one remarkable volume, is the current state of neural science knowledge - ranging from molecules and cells, to anatomic structures and systems, to the senses and cognitive functions-all supported by more than 900 precise, full-color illustrations. In addition to clarifying complex topics, the book also benefits from a cohesive organization, beginning with an insightful overview of the interrelationships between the brain, nervous system, genes, and behavior. Principles of Neural Science then proceeds with an in-depth examination of the molecular and cellular biology of nerve cells, synaptic transmission, and the neural basis of cognition. The remaining sections illuminate how cells, molecules, and systems give us sight, hearing, touch, movement, thought, learning, memories, and emotions. The new fifth edition of Principles of Neural Science is thoroughly updated to reflect the tremendous amount of research, and the very latest clinical perspectives, that have significantly transformed the field within the last decade. Ultimately, Principles of Neural Science affirms that all behavior is an expression of neural activity, and that the future of clinical neurology and psychiatry hinges on the progress of neural science. Far exceeding the scope and scholarship of similar texts, this unmatched guide offers a commanding, scientifically rigorous perspective on the molecular mechanisms of neural function and disease-one that you'll continually rely on to advance your comprehension of brain, mind, and behavior. Features: the cornerstone reference in the field of neuroscience that explains how the nerves, brain, and mind function; clear emphasis on how behavior can be examined through the electrical activity of both individual neurons and systems of nerve cells; current focus on molecular biology as a tool for probing the pathogenesis of many neurological diseases, including muscular dystrophy, Huntington disease, and certain forms of Alzheimer's disease; more than 900 engaging full-color illustrations - including line drawings, radiographs, micrographs, and medical photographs clarify often-complex neuroscience concepts; outstanding section on the development and emergence of behavior, including important coverage of brain damage repair, the sexual differentiation of the nervous system, and the aging brain. Features: more detailed discussions of cognitive and behavioral functions, and an expanded review of cognitive processes; a focus on the increasing importance of computational neural science, which enhances our ability to record the brain's electrical activity and study cognitive processes more directly; and chapter-opening. Key concepts: provides a convenient, study-enhancing introduction to the material covered in each chapter; selected readings and full reference citations at the close of each chapter facilitate further study and research; and helpful appendices highlight basic circuit theory; the neurological examination of the patient; circulation of the brain; the blood-brain barrier, choroid plexus, and cerebrospinal fluid; neural networks; and theoretical approaches to neuroscience.

Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.

Philosophy of Economics: A Contemporary Introduction is the first systematic textbook in the philosophy of economics. It introduces the epistemological, metaphysical and ethical problems that arise in economics, and presents detailed discussions of the solutions that have been offered. Throughout, philosophical issues are illustrated by and analysed in the context of concrete cases drawn from contemporary economics, the history of economic ideas, and actual economic events. This demonstrates the relevance of philosophy of economics both for the science of economics and for the economy. This text will provide an excellent introduction to the philosophy of economics for students and interested general readers alike.

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do. The basic assumption is that vision depends on the eye which is connected to the brain. The author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision -- and what this book is about.

In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose (prediction, explanation or policy analysis) but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences.