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Learn To Create Artificially Intelligent Games Using Python3

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  • 文件大小:13GB
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文件列表

  1. 11 - Introduction to gym module/008 Tennis Game with Random Policy.mp4 204MB
  2. 21 - Deep Convolution Q-Learning Practical Pacman game/006 Build Convolution Neural Network.mp4 194MB
  3. 18 - TicTacToe Tensorflow/003 Preprocess the state.mp4 178MB
  4. 11 - Introduction to gym module/006 Transitional Probability.mp4 172MB
  5. 18 - TicTacToe Tensorflow/009 Creating Neural Network Player.mp4 172MB
  6. 03 - Python Essentials/013 Logical statements.mp4 170MB
  7. 03 - Python Essentials/033 Multiple Inheritance.mp4 159MB
  8. 11 - Introduction to gym module/007 CartPole Example.mp4 149MB
  9. 18 - TicTacToe Tensorflow/002 Creating model for the Game.mp4 142MB
  10. 14 - Creating BlackJack Game/002 Introduction to Project Files.mp4 141MB
  11. 03 - Python Essentials/032 What is Inheritance.mp4 141MB
  12. 18 - TicTacToe Tensorflow/008 TicTacToe Neural Network.mp4 140MB
  13. 03 - Python Essentials/003 Basic Arithmetic in Python.mp4 140MB
  14. 09 - Bellman Equation and Dynamic Programming/012 Temporal Difference.mp4 134MB
  15. 04 - Pygame Refresher/002 Pygame coordinate System.mp4 132MB
  16. 13 - Implementing Monte Carlo Predictions/005 Implementing MC simulation.mp4 130MB
  17. 17 - Tensorflow and Keras/006 Keras models (Important).mp4 123MB
  18. 03 - Python Essentials/031 Constructor in Python.mp4 122MB
  19. 16 - Scratch Implementation of Neural Network/004 Coding dense layer [must know Object Oriented Programming].mp4 121MB
  20. 13 - Implementing Monte Carlo Predictions/001 BlackJack Game and Rules of the Game.mp4 120MB
  21. 15 - Neural Network Refresher/002 Introduction to Neural Networks.mp4 116MB
  22. 21 - Deep Convolution Q-Learning Practical Pacman game/013 Training model for multiple iterations.mp4 115MB
  23. 06 - Creating TicTacToe using MinMax algorithm/006 Implementing MinMax algorithm.mp4 113MB
  24. 21 - Deep Convolution Q-Learning Practical Pacman game/002 Mean Squared Error.mp4 113MB
  25. 03 - Python Essentials/006 Access elements of String.mp4 113MB
  26. 13 - Implementing Monte Carlo Predictions/006 Calculate Value of State using MC simulation.mp4 112MB
  27. 14 - Creating BlackJack Game/010 Training the Q-Learning model and Running Game.mp4 108MB
  28. 10 - Implementation of Q-Learning to Find Optimal Path/002 Introduction to Project Files.mp4 107MB
  29. 15 - Neural Network Refresher/008 Introduction to the Activation Function.mp4 106MB
  30. 14 - Creating BlackJack Game/007 Implementing Temporal Difference (update Q-values).mp4 105MB
  31. 16 - Scratch Implementation of Neural Network/005 Introduction to Activation Function.mp4 105MB
  32. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/002 Action Selection Policy.mp4 104MB
  33. 21 - Deep Convolution Q-Learning Practical Pacman game/001 Introduction to Replay Buffer.mp4 103MB
  34. 03 - Python Essentials/030 Class and Objects Continued.mp4 103MB
  35. 21 - Deep Convolution Q-Learning Practical Pacman game/005 Solving ROM error.mp4 102MB
  36. 21 - Deep Convolution Q-Learning Practical Pacman game/009 Epsilon Greedy (Action-Selection Policy).mp4 101MB
  37. 17 - Tensorflow and Keras/004 Examples.mp4 98MB
  38. 20 - Convolution Neural Network/003 Convolution Layer.mp4 96MB
  39. 03 - Python Essentials/023 Important List Comprehension for Game Development.mp4 96MB
  40. 06 - Creating TicTacToe using MinMax algorithm/008 Playing against AI player and Tuning algorithm.mp4 96MB
  41. 21 - Deep Convolution Q-Learning Practical Pacman game/014 Simulating the game and storing transitions.mp4 96MB
  42. 10 - Implementation of Q-Learning to Find Optimal Path/011 Executing Gameq-Learning Algorithm.mp4 95MB
  43. 16 - Scratch Implementation of Neural Network/002 Coding neuron layer.mp4 95MB
  44. 03 - Python Essentials/022 For loop.mp4 93MB
  45. 04 - Pygame Refresher/003 Introduction to Pygame shape.mp4 93MB
  46. 15 - Neural Network Refresher/006 Updating the weights [partial differentiation].mp4 92MB
  47. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/003 Exploration vs Exploitation.mp4 92MB
  48. 21 - Deep Convolution Q-Learning Practical Pacman game/010 Training the neural network.mp4 90MB
  49. 03 - Python Essentials/015 if else statements.mp4 89MB
  50. 04 - Pygame Refresher/006 Fundamentals of Pygame -- skeleton code.mp4 89MB
  51. 13 - Implementing Monte Carlo Predictions/003 Defining Policy.mp4 86MB
  52. 21 - Deep Convolution Q-Learning Practical Pacman game/008 Build Main Network and Target Network.mp4 84MB
  53. 02 - Setup Anaconda and Install Dependencies for Project/003 Install DependenciesLibraries for the Course.mp4 83MB
  54. 04 - Pygame Refresher/004 Draw shapes using Pygame.mp4 83MB
  55. 09 - Bellman Equation and Dynamic Programming/008 Markov Decision Process + Bellman.mp4 81MB
  56. 03 - Python Essentials/025 Learn to create Functions.mp4 79MB
  57. 04 - Pygame Refresher/010 Make movement within Boundary.mp4 79MB
  58. 14 - Creating BlackJack Game/009 Making AI to play game.mp4 78MB
  59. 15 - Neural Network Refresher/012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp4 78MB
  60. 21 - Deep Convolution Q-Learning Practical Pacman game/004 Creating Environment.mp4 78MB
  61. 11 - Introduction to gym module/009 CartPole with Random Policy.mp4 77MB
  62. 15 - Neural Network Refresher/003 Inspiration and representation for Neural Network.mp4 77MB
  63. 03 - Python Essentials/004 Operations on Numbers.mp4 77MB
  64. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/001 Introduction to Deep Q-Learning.mp4 76MB
  65. 13 - Implementing Monte Carlo Predictions/004 Generating Episodes.mp4 76MB
  66. 10 - Implementation of Q-Learning to Find Optimal Path/010 Implementing Temporal Difference.mp4 75MB
  67. 10 - Implementation of Q-Learning to Find Optimal Path/001 Introduction to Project.mp4 75MB
  68. 09 - Bellman Equation and Dynamic Programming/010 Equation of Q-Learning.mp4 74MB
  69. 12 - Monte Carlo Simulation/003 Monte Carlo Method (MC - method).mp4 73MB
  70. 08 - Key Terms of Artificial Intelligence (Important)/003 Markov Decision Process.mp4 73MB
  71. 10 - Implementation of Q-Learning to Find Optimal Path/005 Example of Q-Table.mp4 73MB
  72. 14 - Creating BlackJack Game/008 AI Player steps.mp4 73MB
  73. 03 - Python Essentials/020 Infinite while loop (Game Loop).mp4 73MB
  74. 06 - Creating TicTacToe using MinMax algorithm/005 Calculating ValueHeuristic for Min Max player.mp4 72MB
  75. 03 - Python Essentials/018 How to access the items from the list.mp4 71MB
  76. 04 - Pygame Refresher/005 Color Picker.mp4 70MB
  77. 15 - Neural Network Refresher/004 History and Application of Neural Network.mp4 70MB
  78. 16 - Scratch Implementation of Neural Network/006 Implementation of activation function [step and sigmoid].mp4 69MB
  79. 16 - Scratch Implementation of Neural Network/001 Setting up environment and coding single neuron.mp4 69MB
  80. 11 - Introduction to gym module/003 Creating Gym Environment.mp4 69MB
  81. 15 - Neural Network Refresher/001 Introduction to Artificial Intelligence.mp4 68MB
  82. 05 - Introduction to MinMax Algorithm/006 Example of Heuristic.mp4 68MB
  83. 10 - Implementation of Q-Learning to Find Optimal Path/004 Briefing about Q-Table.mp4 67MB
  84. 06 - Creating TicTacToe using MinMax algorithm/002 Introduction to Project Files.mp4 67MB
  85. 10 - Implementation of Q-Learning to Find Optimal Path/009 Action Selection Policy (Returning max Q value).mp4 67MB
  86. 21 - Deep Convolution Q-Learning Practical Pacman game/012 Preprocess the state.mp4 65MB
  87. 05 - Introduction to MinMax Algorithm/008 Example of MinMax.mp4 65MB
  88. 12 - Monte Carlo Simulation/001 Why Monte Carlo Simulation is important.mp4 65MB
  89. 02 - Setup Anaconda and Install Dependencies for Project/001 Install Anaconda.mp4 65MB
  90. 03 - Python Essentials/029 Class and Objects.mp4 64MB
  91. 15 - Neural Network Refresher/010 Why we use regularization in the Neural Network.mp4 63MB
  92. 16 - Scratch Implementation of Neural Network/007 Implementation of activation function [tanh and ReLu].mp4 62MB
  93. 03 - Python Essentials/017 Checking type of Data Structures.mp4 62MB
  94. 03 - Python Essentials/007 Formatting strings.mp4 61MB
  95. 17 - Tensorflow and Keras/001 What is Tensorflow.mp4 61MB
  96. 15 - Neural Network Refresher/011 Introduction to the gradient descent [review].mp4 60MB
  97. 05 - Introduction to MinMax Algorithm/007 Introduction to MinMax algorithm.mp4 60MB
  98. 14 - Creating BlackJack Game/005 (State, Action, Reward) of Episodes.mp4 60MB
  99. 03 - Python Essentials/009 Create Variables in Python.mp4 60MB
  100. 06 - Creating TicTacToe using MinMax algorithm/003 Creating Indecisive Player (Random).mp4 59MB
  101. 03 - Python Essentials/026 Learn about return statements.mp4 58MB
  102. 18 - TicTacToe Tensorflow/005 Training the model.mp4 58MB
  103. 10 - Implementation of Q-Learning to Find Optimal Path/006 Q-Agent.mp4 57MB
  104. 09 - Bellman Equation and Dynamic Programming/005 Example.mp4 57MB
  105. 09 - Bellman Equation and Dynamic Programming/006 Plan.mp4 57MB
  106. 05 - Introduction to MinMax Algorithm/001 Introduction to Board Games.mp4 57MB
  107. 15 - Neural Network Refresher/007 Introduction to partial differentiation.mp4 56MB
  108. 17 - Tensorflow and Keras/002 Rank of Tensors.mp4 56MB
  109. 07 - Introduction to Artificial Intelligence/009 Value of the State.mp4 56MB
  110. 05 - Introduction to MinMax Algorithm/004 Solution of Lookahead problem.mp4 53MB
  111. 09 - Bellman Equation and Dynamic Programming/004 Bellman Equation.mp4 53MB
  112. 06 - Creating TicTacToe using MinMax algorithm/007 Setting up Autoplayer (Artificial Intelligent Player).mp4 53MB
  113. 10 - Implementation of Q-Learning to Find Optimal Path/003 Creating Environment.mp4 53MB
  114. 20 - Convolution Neural Network/006 BackPropagation.mp4 53MB
  115. 04 - Pygame Refresher/001 Introduction to the pygame.mp4 51MB
  116. 04 - Pygame Refresher/008 Movement of the shapes.mp4 50MB
  117. 04 - Pygame Refresher/007 Render a rectangle in the Screen.mp4 50MB
  118. 15 - Neural Network Refresher/005 Example of neural network.mp4 49MB
  119. 09 - Bellman Equation and Dynamic Programming/007 Non Deterministic Environment.mp4 49MB
  120. 16 - Scratch Implementation of Neural Network/003 Using dot product to code neuron layer.mp4 49MB
  121. 07 - Introduction to Artificial Intelligence/002 Reinforcement Learning.mp4 49MB
  122. 11 - Introduction to gym module/005 State space and Action space.mp4 49MB
  123. 03 - Python Essentials/011 Learn to create conditions.mp4 48MB
  124. 06 - Creating TicTacToe using MinMax algorithm/004 Implementing MinMax.mp4 48MB
  125. 21 - Deep Convolution Q-Learning Practical Pacman game/003 Main Network and Target Network.mp4 48MB
  126. 13 - Implementing Monte Carlo Predictions/002 Creating BlackJack Environment.mp4 47MB
  127. 11 - Introduction to gym module/002 Example of Gym Environment.mp4 47MB
  128. 17 - Tensorflow and Keras/007 Implementing Neural Network using Keras.mp4 47MB
  129. 21 - Deep Convolution Q-Learning Practical Pacman game/015 Testing the game.mp4 46MB
  130. 05 - Introduction to MinMax Algorithm/002 Tree representation of Game.mp4 46MB
  131. 09 - Bellman Equation and Dynamic Programming/009 Introduction to Q-Learning.mp4 45MB
  132. 18 - TicTacToe Tensorflow/004 Define Independent (input) and Dependent (output) Variable.mp4 45MB
  133. 11 - Introduction to gym module/004 Getting started with Gym.mp4 45MB
  134. 14 - Creating BlackJack Game/001 Action Selection Policy (Epsilon-Greedy).mp4 45MB
  135. 15 - Neural Network Refresher/009 Why do we need bias in the program.mp4 45MB
  136. 06 - Creating TicTacToe using MinMax algorithm/001 introduction to Game.mp4 44MB
  137. 17 - Tensorflow and Keras/003 Program Elements of Tensorflow.mp4 44MB
  138. 14 - Creating BlackJack Game/004 Implementing Epsilon Greedy Policy.mp4 43MB
  139. 18 - TicTacToe Tensorflow/001 Introduction to Project Files.mp4 43MB
  140. 03 - Python Essentials/027 Introduction to the section.mp4 41MB
  141. 17 - Tensorflow and Keras/005 Introduction to Keras.mp4 41MB
  142. 02 - Setup Anaconda and Install Dependencies for Project/004 Download Visual Studio Code.mp4 41MB
  143. 05 - Introduction to MinMax Algorithm/009 MinMax Example for TicTacToe.mp4 41MB
  144. 14 - Creating BlackJack Game/006 Introduction to Discount Parameter.mp4 40MB
  145. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/004 Deep Convolution Q-Learning.mp4 40MB
  146. 12 - Monte Carlo Simulation/004 First Visit vs Every Visit MC.mp4 40MB
  147. 02 - Setup Anaconda and Install Dependencies for Project/002 Create Virtual Environment.mp4 40MB
  148. 08 - Key Terms of Artificial Intelligence (Important)/001 Markov Property and Markov Chain.mp4 39MB
  149. 21 - Deep Convolution Q-Learning Practical Pacman game/011 Fit the model.mp4 39MB
  150. 04 - Pygame Refresher/009 Smoothen the movement using FPS.mp4 38MB
  151. 03 - Python Essentials/028 What is Object Oriented Programming.mp4 38MB
  152. 10 - Implementation of Q-Learning to Find Optimal Path/007 Possible Actions.mp4 38MB
  153. 05 - Introduction to MinMax Algorithm/005 Heuristic Evaluation of Board.mp4 37MB
  154. 03 - Python Essentials/021 Finite Game Loop.mp4 36MB
  155. 21 - Deep Convolution Q-Learning Practical Pacman game/007 Store Transition in Replay buffer.mp4 36MB
  156. 03 - Python Essentials/016 Introduction to Data Structures.mp4 35MB
  157. 01 - Introduction/001 Introduction.mp4 34MB
  158. 10 - Implementation of Q-Learning to Find Optimal Path/008 Iterations.mp4 32MB
  159. 09 - Bellman Equation and Dynamic Programming/003 Value Function.mp4 32MB
  160. 20 - Convolution Neural Network/001 Introduction to convolution neural network.mp4 31MB
  161. 18 - TicTacToe Tensorflow/007 TicTacToe Model.mp4 30MB
  162. 05 - Introduction to MinMax Algorithm/003 Lookahead Problem.mp4 30MB
  163. 14 - Creating BlackJack Game/003 Q-Table.mp4 30MB
  164. 20 - Convolution Neural Network/002 How ConvNet works.mp4 29MB
  165. 08 - Key Terms of Artificial Intelligence (Important)/002 Markov Reward Process.mp4 29MB
  166. 12 - Monte Carlo Simulation/002 Monte Carlo Simulation.mp4 27MB
  167. 03 - Python Essentials/005 Introduction to Strings in Python.mp4 26MB
  168. 03 - Python Essentials/002 Introduction to the data types.mp4 25MB
  169. 11 - Introduction to gym module/001 The gym module.mp4 24MB
  170. 03 - Python Essentials/001 What is Python.mp4 23MB
  171. 12 - Monte Carlo Simulation/005 BlackJack Example.mp4 22MB
  172. 07 - Introduction to Artificial Intelligence/001 Motivation for Artificial Intelligence.mp4 22MB
  173. 03 - Python Essentials/010 Introduction to Booleans in Python.mp4 20MB
  174. 20 - Convolution Neural Network/004 RELU Layer.mp4 19MB
  175. 07 - Introduction to Artificial Intelligence/007 Policy.mp4 19MB
  176. 07 - Introduction to Artificial Intelligence/010 Model.mp4 18MB
  177. 07 - Introduction to Artificial Intelligence/006 Typical RL scenario.mp4 18MB
  178. 03 - Python Essentials/014 Introduction to conditional statements.mp4 17MB
  179. 09 - Bellman Equation and Dynamic Programming/001 Introduction.mp4 17MB
  180. 20 - Convolution Neural Network/005 Pooling Layer.mp4 16MB
  181. 15 - Neural Network Refresher/013 Introduction to mini-batch SGD.mp4 16MB
  182. 09 - Bellman Equation and Dynamic Programming/002 Tribute to Bellman.mp4 15MB
  183. 03 - Python Essentials/008 Introduction to the variables.mp4 14MB
  184. 03 - Python Essentials/012 is operator in Python.mp4 14MB
  185. 07 - Introduction to Artificial Intelligence/004 Rewards.mp4 13MB
  186. 03 - Python Essentials/024 What is Function and Why we need it.mp4 12MB
  187. 03 - Python Essentials/019 Introduction to the loops in Python.mp4 12MB
  188. 09 - Bellman Equation and Dynamic Programming/011 Q value for Non-Deterministic Environment.mp4 12MB
  189. 18 - TicTacToe Tensorflow/006 Predict from the model.mp4 11MB
  190. 07 - Introduction to Artificial Intelligence/003 Environment.mp4 11MB
  191. 05 - Introduction to MinMax Algorithm/010 MinMax Algorithm.mp4 9MB
  192. 07 - Introduction to Artificial Intelligence/005 Path.mp4 5MB
  193. 07 - Introduction to Artificial Intelligence/008 Rewards.mp4 5MB
  194. 09 - Bellman Equation and Dynamic Programming/012 Temporal Difference_en.vtt 35KB
  195. 11 - Introduction to gym module/006 Transitional Probability_en.vtt 33KB
  196. 18 - TicTacToe Tensorflow/003 Preprocess the state_en.vtt 32KB
  197. 21 - Deep Convolution Q-Learning Practical Pacman game/006 Build Convolution Neural Network_en.vtt 32KB
  198. 11 - Introduction to gym module/008 Tennis Game with Random Policy_en.vtt 30KB
  199. 15 - Neural Network Refresher/002 Introduction to Neural Networks_en.vtt 29KB
  200. 15 - Neural Network Refresher/008 Introduction to the Activation Function_en.vtt 29KB
  201. 21 - Deep Convolution Q-Learning Practical Pacman game/002 Mean Squared Error_en.vtt 27KB
  202. 18 - TicTacToe Tensorflow/002 Creating model for the Game_en.vtt 26KB
  203. 03 - Python Essentials/003 Basic Arithmetic in Python_en.vtt 24KB
  204. 16 - Scratch Implementation of Neural Network/004 Coding dense layer [must know Object Oriented Programming]_en.vtt 24KB
  205. 03 - Python Essentials/013 Logical statements_en.vtt 23KB
  206. 15 - Neural Network Refresher/006 Updating the weights [partial differentiation]_en.vtt 22KB
  207. 13 - Implementing Monte Carlo Predictions/005 Implementing MC simulation_en.vtt 22KB
  208. 21 - Deep Convolution Q-Learning Practical Pacman game/001 Introduction to Replay Buffer_en.vtt 21KB
  209. 15 - Neural Network Refresher/012 Introduction to Stochastic Gradient Descent and Adam Optimizer_en.vtt 21KB
  210. 16 - Scratch Implementation of Neural Network/002 Coding neuron layer_en.vtt 21KB
  211. 18 - TicTacToe Tensorflow/009 Creating Neural Network Player_en.vtt 21KB
  212. 18 - TicTacToe Tensorflow/008 TicTacToe Neural Network_en.vtt 21KB
  213. 04 - Pygame Refresher/002 Pygame coordinate System_en.vtt 20KB
  214. 14 - Creating BlackJack Game/002 Introduction to Project Files_en.vtt 20KB
  215. 06 - Creating TicTacToe using MinMax algorithm/006 Implementing MinMax algorithm_en.vtt 19KB
  216. 09 - Bellman Equation and Dynamic Programming/008 Markov Decision Process + Bellman_en.vtt 19KB
  217. 11 - Introduction to gym module/007 CartPole Example_en.vtt 19KB
  218. 15 - Neural Network Refresher/007 Introduction to partial differentiation_en.vtt 19KB
  219. 15 - Neural Network Refresher/011 Introduction to the gradient descent [review]_en.vtt 19KB
  220. 16 - Scratch Implementation of Neural Network/001 Setting up environment and coding single neuron_en.vtt 19KB
  221. 03 - Python Essentials/023 Important List Comprehension for Game Development_en.vtt 19KB
  222. 16 - Scratch Implementation of Neural Network/005 Introduction to Activation Function_en.vtt 19KB
  223. 03 - Python Essentials/033 Multiple Inheritance_en.vtt 18KB
  224. 13 - Implementing Monte Carlo Predictions/001 BlackJack Game and Rules of the Game_en.vtt 18KB
  225. 15 - Neural Network Refresher/004 History and Application of Neural Network_en.vtt 17KB
  226. 05 - Introduction to MinMax Algorithm/007 Introduction to MinMax algorithm_en.vtt 17KB
  227. 21 - Deep Convolution Q-Learning Practical Pacman game/013 Training model for multiple iterations_en.vtt 17KB
  228. 03 - Python Essentials/022 For loop_en.vtt 17KB
  229. 15 - Neural Network Refresher/003 Inspiration and representation for Neural Network_en.vtt 17KB
  230. 21 - Deep Convolution Q-Learning Practical Pacman game/010 Training the neural network_en.vtt 17KB
  231. 20 - Convolution Neural Network/003 Convolution Layer_en.vtt 17KB
  232. 09 - Bellman Equation and Dynamic Programming/010 Equation of Q-Learning_en.vtt 16KB
  233. 16 - Scratch Implementation of Neural Network/007 Implementation of activation function [tanh and ReLu]_en.vtt 16KB
  234. 15 - Neural Network Refresher/005 Example of neural network_en.vtt 16KB
  235. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/001 Introduction to Deep Q-Learning_en.vtt 16KB
  236. 10 - Implementation of Q-Learning to Find Optimal Path/002 Introduction to Project Files_en.vtt 16KB
  237. 14 - Creating BlackJack Game/007 Implementing Temporal Difference (update Q-values)_en.vtt 15KB
  238. 14 - Creating BlackJack Game/010 Training the Q-Learning model and Running Game_en.vtt 15KB
  239. 13 - Implementing Monte Carlo Predictions/006 Calculate Value of State using MC simulation_en.vtt 15KB
  240. 09 - Bellman Equation and Dynamic Programming/004 Bellman Equation_en.vtt 15KB
  241. 21 - Deep Convolution Q-Learning Practical Pacman game/009 Epsilon Greedy (Action-Selection Policy)_en.vtt 15KB
  242. 16 - Scratch Implementation of Neural Network/006 Implementation of activation function [step and sigmoid]_en.vtt 15KB
  243. 05 - Introduction to MinMax Algorithm/006 Example of Heuristic_en.vtt 15KB
  244. 03 - Python Essentials/020 Infinite while loop (Game Loop)_en.vtt 14KB
  245. 05 - Introduction to MinMax Algorithm/008 Example of MinMax_en.vtt 14KB
  246. 21 - Deep Convolution Q-Learning Practical Pacman game/014 Simulating the game and storing transitions_en.vtt 14KB
  247. 03 - Python Essentials/004 Operations on Numbers_en.vtt 14KB
  248. 03 - Python Essentials/032 What is Inheritance_en.vtt 14KB
  249. 05 - Introduction to MinMax Algorithm/001 Introduction to Board Games_en.vtt 14KB
  250. 09 - Bellman Equation and Dynamic Programming/005 Example_en.vtt 14KB
  251. 10 - Implementation of Q-Learning to Find Optimal Path/011 Executing Gameq-Learning Algorithm_en.vtt 14KB
  252. 03 - Python Essentials/031 Constructor in Python_en.vtt 14KB
  253. 04 - Pygame Refresher/010 Make movement within Boundary_en.vtt 14KB
  254. 03 - Python Essentials/030 Class and Objects Continued_en.vtt 14KB
  255. 10 - Implementation of Q-Learning to Find Optimal Path/005 Example of Q-Table_en.vtt 13KB
  256. 21 - Deep Convolution Q-Learning Practical Pacman game/008 Build Main Network and Target Network_en.vtt 13KB
  257. 03 - Python Essentials/007 Formatting strings_en.vtt 13KB
  258. 13 - Implementing Monte Carlo Predictions/003 Defining Policy_en.vtt 13KB
  259. 21 - Deep Convolution Q-Learning Practical Pacman game/005 Solving ROM error_en.vtt 13KB
  260. 10 - Implementation of Q-Learning to Find Optimal Path/009 Action Selection Policy (Returning max Q value)_en.vtt 13KB
  261. 17 - Tensorflow and Keras/006 Keras models (Important)_en.vtt 13KB
  262. 12 - Monte Carlo Simulation/003 Monte Carlo Method (MC - method)_en.vtt 12KB
  263. 04 - Pygame Refresher/006 Fundamentals of Pygame -- skeleton code_en.vtt 12KB
  264. 06 - Creating TicTacToe using MinMax algorithm/008 Playing against AI player and Tuning algorithm_en.vtt 12KB
  265. 16 - Scratch Implementation of Neural Network/003 Using dot product to code neuron layer_en.vtt 12KB
  266. 11 - Introduction to gym module/003 Creating Gym Environment_en.vtt 12KB
  267. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/002 Action Selection Policy_en.vtt 12KB
  268. 06 - Creating TicTacToe using MinMax algorithm/003 Creating Indecisive Player (Random)_en.vtt 12KB
  269. 03 - Python Essentials/025 Learn to create Functions_en.vtt 12KB
  270. 17 - Tensorflow and Keras/004 Examples_en.vtt 12KB
  271. 15 - Neural Network Refresher/001 Introduction to Artificial Intelligence_en.vtt 12KB
  272. 03 - Python Essentials/006 Access elements of String_en.vtt 12KB
  273. 13 - Implementing Monte Carlo Predictions/004 Generating Episodes_en.vtt 12KB
  274. 10 - Implementation of Q-Learning to Find Optimal Path/006 Q-Agent_en.vtt 12KB
  275. 12 - Monte Carlo Simulation/001 Why Monte Carlo Simulation is important_en.vtt 12KB
  276. 09 - Bellman Equation and Dynamic Programming/007 Non Deterministic Environment_en.vtt 12KB
  277. 05 - Introduction to MinMax Algorithm/004 Solution of Lookahead problem_en.vtt 12KB
  278. 15 - Neural Network Refresher/009 Why do we need bias in the program_en.vtt 11KB
  279. 21 - Deep Convolution Q-Learning Practical Pacman game/003 Main Network and Target Network_en.vtt 11KB
  280. 10 - Implementation of Q-Learning to Find Optimal Path/001 Introduction to Project_en.vtt 11KB
  281. 06 - Creating TicTacToe using MinMax algorithm/005 Calculating ValueHeuristic for Min Max player_en.vtt 11KB
  282. 11 - Introduction to gym module/009 CartPole with Random Policy_en.vtt 11KB
  283. 03 - Python Essentials/018 How to access the items from the list_en.vtt 11KB
  284. 03 - Python Essentials/009 Create Variables in Python_en.vtt 11KB
  285. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/003 Exploration vs Exploitation_en.vtt 11KB
  286. 03 - Python Essentials/015 if else statements_en.vtt 11KB
  287. 17 - Tensorflow and Keras/002 Rank of Tensors_en.vtt 11KB
  288. 06 - Creating TicTacToe using MinMax algorithm/002 Introduction to Project Files_en.vtt 11KB
  289. 09 - Bellman Equation and Dynamic Programming/009 Introduction to Q-Learning_en.vtt 11KB
  290. 08 - Key Terms of Artificial Intelligence (Important)/003 Markov Decision Process_en.vtt 10KB
  291. 21 - Deep Convolution Q-Learning Practical Pacman game/004 Creating Environment_en.vtt 10KB
  292. 04 - Pygame Refresher/004 Draw shapes using Pygame_en.vtt 10KB
  293. 05 - Introduction to MinMax Algorithm/002 Tree representation of Game_en.vtt 10KB
  294. 14 - Creating BlackJack Game/008 AI Player steps_en.vtt 10KB
  295. 10 - Implementation of Q-Learning to Find Optimal Path/010 Implementing Temporal Difference_en.vtt 10KB
  296. 03 - Python Essentials/017 Checking type of Data Structures_en.vtt 10KB
  297. 14 - Creating BlackJack Game/005 (State, Action, Reward) of Episodes_en.vtt 10KB
  298. 14 - Creating BlackJack Game/009 Making AI to play game_en.vtt 10KB
  299. 21 - Deep Convolution Q-Learning Practical Pacman game/012 Preprocess the state_en.vtt 10KB
  300. 17 - Tensorflow and Keras/001 What is Tensorflow_en.vtt 10KB
  301. 14 - Creating BlackJack Game/004 Implementing Epsilon Greedy Policy_en.vtt 10KB
  302. 18 - TicTacToe Tensorflow/005 Training the model_en.vtt 10KB
  303. 05 - Introduction to MinMax Algorithm/009 MinMax Example for TicTacToe_en.vtt 9KB
  304. 09 - Bellman Equation and Dynamic Programming/006 Plan_en.vtt 9KB
  305. 03 - Python Essentials/029 Class and Objects_en.vtt 9KB
  306. 19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/004 Deep Convolution Q-Learning_en.vtt 9KB
  307. 10 - Implementation of Q-Learning to Find Optimal Path/003 Creating Environment_en.vtt 9KB
  308. 03 - Python Essentials/026 Learn about return statements_en.vtt 9KB
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  323. 20 - Convolution Neural Network/006 BackPropagation_en.vtt 8KB
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  334. 03 - Python Essentials/011 Learn to create conditions_en.vtt 7KB
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