چگونه مشتریان طراحی وب پیدا کنیم: و چگونه مشتریان جدید جذب کنیم! چگونه یک کسب‌وکار طراحی وب راه‌اندازی کنیم، چگونه مشتریان بیشتری بگیریم و مشتریان جدید پیدا کنیم و جستجوی مشتریان جدید

The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI. Preface Contents Part I: Artificial Intelligence Chapter 1 Introduction 1.1 What Is AI? 1.2 The Foundations of Artificial Intelligence 1.3 The History of Artificial Intelligence 1.4 The State of the Art 1.5 Risks and Benefits of AI Summary Bibliographical and Historical Notes Chapter 2 Intelligent Agents 2.1 Agents and Environments 2.2 Good Behavior: The Concept of Rationality 2.3 The Nature of Environments 2.4 The Structure of Agents Summary Bibliographical and Historical Notes Part II: Problem-solving Chapter 3 Solving Problems by Searching 3.1 Problem-Solving Agents 3.2 Example Problems 3.3 Search Algorithms 3.4 Uninformed Search Strategies 3.5 Informed (Heuristic) Search Strategies 3.6 Heuristic Functions Summary Bibliographical and Historical Notes Chapter 4 Search in Complex Environments 4.1 Local Search and Optimization Problems 4.2 Local Search in Continuous Spaces 4.3 Search with Nondeterministic Actions 4.4 Search in Partially Observable Environments 4.5 Online Search Agents and Unknown Environments Summary Bibliographical and Historical Notes Chapter 5 Adversarial Search and Games 5.1 Game Theory 5.2 Optimal Decisions in Games 5.3 Heuristic Alpha--Beta Tree Search 5.4 Monte Carlo Tree Search 5.5 Stochastic Games 5.6 Partially Observable Games 5.7 Limitations of Game Search Algorithms Summary Bibliographical and Historical Notes Chapter 6 Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems 6.2 Constraint Propagation: Inference in CSPs 6.3 Backtracking Search for CSPs 6.4 Local Search for CSPs 6.5 The Structure of Problems Summary Bibliographical and Historical Notes Part III: Knowledge, reasoning, and planning Chapter 7 Logical Agents 7.1 Knowledge-Based Agents 7.2 The Wumpus World 7.3 Logic 7.4 Propositional Logic: A Very Simple Logic 7.5 Propositional Theorem Proving 7.6 Effective Propositional Model Checking 7.7 Agents Based on Propositional Logic Summary Bibliographical and Historical Notes Chapter 8 First-Order Logic 8.1 Representation Revisited 8.2 Syntax and Semantics of First-Order Logic 8.3 Using First-Order Logic 8.4 Knowledge Engineering in First-Order Logic Summary Bibliographical and Historical Notes Chapter 9 Inference in First-Order Logic 9.1 Propositional vs. First-Order Inference 9.2 Unification and First-Order Inference 9.3 Forward Chaining 9.4 Backward Chaining 9.5 Resolution Summary Bibliographical and Historical Notes Chapter 10 Knowledge Representation 10.1 Ontological Engineering 10.2 Categories and Objects 10.3 Events 10.4 Mental Objects and Modal Logic 10.5 Reasoning Systems for Categories 10.6 Reasoning with Default Information Summary Bibliographical and Historical Notes Chapter 11 Automated Planning 11.1 Definition of Classical Planning 11.2 Algorithms for Classical Planning 11.3 Heuristics for Planning 11.4 Hierarchical Planning 11.5 Planning and Acting in Nondeterministic Domains 11.6 Time, Schedules, and Resources 11.7 Analysis of Planning Approaches Summary Bibliographical and Historical Notes Part IV: Uncertain knowledge and reasoning Chapter 12 Quantifying Uncertainty 12.1 Acting under Uncertainty 12.2 Basic Probability Notation 12.3 Inference Using Full Joint Distributions 12.4 Independence 12.5 Bayes' Rule and Its Use 12.6 Naive Bayes Models 12.7 The Wumpus World Revisited Summary Bibliographical and Historical Notes Chapter 13 Probabilistic Reasoning 13.1 Representing Knowledge in an Uncertain Domain 13.2 The Semantics of Bayesian Networks 13.3 Exact Inference in Bayesian Networks 13.4 Approximate Inference for Bayesian Networks 13.5 Causal Networks Summary Bibliographical and Historical Notes Chapter 14 Probabilistic Reasoning over Time 14.1 Time and Uncertainty 14.2 Inference in Temporal Models 14.3 Hidden Markov Models 14.4 Kalman Filters 14.5 Dynamic Bayesian Networks Summary Bibliographical and Historical Notes Chapter 15 Probabilistic Programming 15.1 Relational Probability Models 15.2 Open-Universe Probability Models 15.3 Keeping Track of a Complex World 15.4 Programs as Probability Models Summary Bibliographical and Historical Notes Chapter 16 Making Simple Decisions 16.1 Combining Beliefs and Desires under Uncertainty 16.2 The Basis of Utility Theory 16.3 Utility Functions 16.4 Multiattribute Utility Functions 16.5 Decision Networks 16.6 The Value of Information 16.7 Unknown Preferences Summary Bibliographical and Historical Notes Chapter 17 Making Complex Decisions 17.1 Sequential Decision Problems 17.2 Algorithms for MDPs 17.3 Bandit Problems 17.4 Partially Observable MDPs 17.5 Algorithms for Solving POMDPs Summary Bibliographical and Historical Notes Chapter 18 Multiagent Decision Making 18.1 Properties of Multiagent Environments 18.2 Non-Cooperative Game Theory 18.3 Cooperative Game Theory 18.4 Making Collective Decisions Summary Bibliographical and Historical Notes Part V: Machine Learning Chapter 19 Learning from Examples 19.1 Forms of Learning 19.2 Supervised Learning 19.3 Learning Decision Trees 19.4 Model Selection and Optimization 19.5 The Theory of Learning 19.6 Linear Regression and Classification 19.7 Nonparametric Models 19.8 Ensemble Learning 19.9 Developing Machine Learning Systems Summary Bibliographical and Historical Notes Chapter 20 Learning Probabilistic Models 20.1 Statistical Learning 20.2 Learning with Complete Data 20.3 Learning with Hidden Variables: The EM Algorithm Summary Bibliographical and Historical Notes Chapter 21 Deep Learning 21.1 Simple Feedforward Networks 21.2 Computation Graphs for Deep Learning 21.3 Convolutional Networks 21.4 Learning Algorithms 21.5 Generalization 21.6 Recurrent Neural Networks 21.7 Unsupervised Learning and Transfer Learning 21.8 Applications Summary Bibliographical and Historical Notes Chapter 22 Reinforcement Learning 22.1 Learning from Rewards 22.2 Passive Reinforcement Learning 22.3 Active Reinforcement Learning 22.4 Generalization in Reinforcement Learning 22.5 Policy Search 22.6 Apprenticeship and Inverse Reinforcement Learning 22.7 Applications of Reinforcement Learning Summary Bibliographical and Historical Notes Part VI: Communicating, perceiving, and acting Chapter 23 Natural Language Processing 23.1 Language Models 23.2 Grammar 23.3 Parsing 23.4 Augmented Grammars 23.5 Complications of Real Natural Language 23.6 Natural Language Tasks Summary Bibliographical and Historical Notes Chapter 24 Deep Learning for Natural Language Processing 24.1 Word Embeddings 24.2 Recurrent Neural Networks for NLP 24.3 Sequence-to-Sequence Models 24.4 The Transformer Architecture 24.5 Pretraining and Transfer Learning 24.6 State of the art Summary Bibliographical and Historical Notes Chapter 25 Computer Vision 25.1 Introduction 25.2 Image Formation 25.3 Simple Image Features 25.4 Classifying Images 25.5 Detecting Objects 25.6 The 3D World 25.7 Using Computer Vision Summary Bibliographical and Historical Notes Chapter 26 Robotics 26.1 Robots 26.2 Robot Hardware 26.3 What kind of problem is robotics solving? 26.4 Robotic Perception 26.5 Planning and Control 26.6 Planning Uncertain Movements 26.7 Reinforcement Learning in Robotics 26.8 Humans and Robots 26.9 Alternative Robotic Frameworks 26.10 Application Domains Summary Bibliographical and Historical Notes Part VII: Conclusions Chapter 27 Philosophy, Ethics, and Safety of AI 27.1 The Limits of AI 27.2 Can Machines Really Think? 27.3 The Ethics of AI Summary Bibliographical and Historical Notes Chapter 28 The Future of AI 28.1 AI Components 28.2 AI Architectures Appendix A: Mathematical Background A.1 Complexity Analysis and O() Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability Distributions Bibliographical and Historical Notes Appendix B: Notes on Languages and Algorithms B.1 Defining Languages with Backus--Naur Form (BNF) B.2 Describing Algorithms with Pseudocode B.3 Online Supplemental Material Bibliography Index