다운로드 Artificial Intelligence App PC 용

PC를 설정하고 Windows 11에서 Artificial Intelligence App 앱을 다운로드하는 단계:

1. 컴퓨터가 호환되는지 확인: 최소 요구 사항은 다음과 같습니다.
• RAM: 8GB (최저한의), 16GB (추천)
• 저장: SSD
• Processor:
• Intel Core i3 8th Gen (최소 이상)
• AMD Ryzen 3000 (최소 이상)
• Qualcomm Snapdragon 8c (최소 이상)
• Processor Architecture: x64 or ARM64
2. Artificial Intelligence App 에 이미 기본 Windows 버전이 있는지 확인하십시오. 여기서 하세요 ». 기본 버전이 없으면 3단계로 진행합니다.

This Artificial Intelligence app is a complete free handbook of Artificial Intelligence with diagrams and graphs. It is part of Computer science or software engineering education which brings important topics, notes, news & blog on the subject. The App serves as a quick reference guide on this engineering subject. It covers more than 600 topics of Artificial Intelligence.The topics are divided into 5 units. 1. Introduction to AI 2. Problem Solving with AI 3. knowledge and Reasoning 4. Learning 5. Natural Response Processing Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. The App will provide faster learning and quick revisions on the subject. Few Additional subjects which have been included in the app are Automata Neural network fuzzy systems Real-time Systems Some of the topics Covered in this application are: 1. Turing test 2. Introduction to Artificial Intelligence 3. History of AI 4. The AI Cycle 5. Knowledge Representation 6. Typical AI problems 7. Limits of AI 8. Introduction to Agents 9. Agent Performance 10. Intelligent Agents 11. Structure Of Intelligent Agents 12. Types of agent program 13. Goal based Agents 14. Utility-based agents 15. Agents and environments 16. Agent architectures 17. Search for Solutions 18. State Spaces 19. Graph Searching 20. A Generic Searching Algorithm 21. Uninformed Search Strategies 22. Breadth-First Search 23. Heuristic Search 24. A∗ Search 25. Search Tree 26. Depth first Search 27. Properties of Depth First Search 28. Bi-directional search 29. Search Graphs 30. Informed Search Strategies 31. Methods of Informed Search 32. Greedy Search 33. Proof of Admissibility of A* 34. Properties of Heuristics 35. Iterative-Deepening A* 36. Other Memory limited heuristic search 37. N-Queens eample 38. Adversarial Search 39. Genetic Algorithms 40. Games 41. Optimal decisions in Games 42. minimax algorithm 43. Alpha Beta Pruning 44. Backtracking 45. Consistency Driven Techniques 46. Path Consistency (K-Consistency) 47. Look Ahead 48. Propositional Logic 49. Syntax of Propositional Calculus 50. Knowledge Representation and Reasoning 51. Propositional Logic Inference 52. Propositional Definite Clauses 53. Knowledge-Level Debugging 54. Rules of Inference 55. Soundness and Completeness 56. First Order Logic 57. Unification 58. Semantics 59. Herbrand Universe 60. Soundness, Completeness, Consistency, Satisfiability 61. Resolution 62. Herbrand Revisited 63. Proof as Search 64. Some Proof Strategies 65. Non-Monotonic Reasoning 66. Truth Maintenance Systems 67. Rule Based Systems 68. Pure Prolog 69. Forward chaining 70. backward Chaining 71. Choice between forward and backward chaining 72. AND/OR Trees 73. Hidden Markov Model 74. Bayesian networks 75. Learning Issues 76. Supervised Learning 77. Decision Trees 78. Knowledge Representation Formalisms 79. Semantic Networks 80. Inference in a Semantic Net 81. Extending Semantic Nets 82. Frames 83. Slots as Objects 84. Interpreting frames 85. Introduction to Planning 86. Problem Solving vs. Planning 87. Logic Based Planning 88. Planning Systems 89. Planning as Search 90. Situation-Space Planning Algorithms 91. Partial-Order Planning 92. Plan-Space Planning Algorithms 93. Interleaving vs. Non-Interleaving of Sub-Plan Steps 94. Simple Sock/Shoe Example 95. Probabilistic Reasoning 96. Review of Probability Theory 97. Semantics of Bayesian Networks 98. Introduction to Learning 99. Taxonomy of Learning Systems 100. Mathematical formulation of the inductive learning problem AI is going to be one of the most important technologies in the coming days. It is a must have study for engineering, computer science, software engineering and other cognitive science students. It also going to be extremely important for mechanical, Automotive & electrical engineering students and Professionals. Download the app for the introduction to AI and related technology.