다운로드 Artificial Intelligence App

다른 한편에서는 원활한 경험을하려면 파일을 장치에 다운로드 한 후 파일을 사용하는 방법을 알아야합니다. APK 파일은 Android 앱의 원시 파일이며 Android 패키지 키트를 의미합니다. 모바일 앱 배포 및 설치를 위해 Android 운영 체제에서 사용하는 패키지 파일 형식입니다.
네 가지 간단한 단계에서 사용 방법을 알려 드리겠습니다. Artificial Intelligence App 귀하의 전화 번호.

1 단계 : 다운로드 Artificial Intelligence App 귀하의 기기에서

아래의 다운로드 미러를 사용하여 지금 당장이 작업을 수행 할 수 있습니다. 그것의 99 % 보장 . 컴퓨터에서 파일을 다운로드하는 경우, 그것을 안드로이드 장치로 옮기십시오.  

2 단계 : 기기에 타사 앱 허용

설치하려면 Artificial Intelligence App 타사 응용 프로그램이 현재 설치 소스로 활성화되어 있는지 확인해야합니다. 메뉴 > 설정 > 보안> 으로 이동하여 알 수없는 소스 를 선택하여 휴대 전화가 Google Play 스토어 이외의 소스에서 앱을 설치하도록 허용하십시오.

3 단계 : 파일 관리자로 이동

이제 위치를 찾으십시오 Artificial Intelligence App 방금 다운로드 한 파일입니다.
일단 당신이 Artificial Intelligence App 파일을 클릭하면 일반 설치 프로세스가 시작됩니다. 메시지가 나타나면 "예" 를 누르십시오. 그러나 화면의 모든 메시지를 읽으십시오.

4 단계 : 즐기십시오

Artificial Intelligence App 이 (가) 귀하의 기기에 설치되었습니다. 즐겨!

개발자 설명

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.