Learn To Create Artificially Intelligent Games Using Python3 | 02EE36FB1A2BA0F6967861ECFEFDA14652591BD4

Learn To Create Artificially Intelligent Games Using Python3

Name	Learn To Create Artificially Intelligent Games Using Python3	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	13.5 GB
Total Files	387
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Hash	02EE36FB1A2BA0F6967861ECFEFDA14652591BD4

/01 - Introduction/
001 Introduction.mp4	35.4 MB
01 - Introduction/
001 Introduction_en.vtt	1.7 KB
02 - Setup Anaconda and Install Dependencies for Project/
003 Install DependenciesLibraries for the Course.mp4	87.4 MB
001 Install Anaconda.mp4	67.7 MB
004 Download Visual Studio Code.mp4	43.2 MB
002 Create Virtual Environment.mp4	41.5 MB
004 Download Visual Studio Code_en.vtt	8.9 KB
003 Install DependenciesLibraries for the Course_en.vtt	8.5 KB
001 Install Anaconda_en.vtt	6.9 KB
002 Create Virtual Environment_en.vtt	5.7 KB
03 - Python Essentials/
013 Logical statements.mp4	177.8 MB
033 Multiple Inheritance.mp4	167.1 MB
032 What is Inheritance.mp4	147.7 MB
003 Basic Arithmetic in Python.mp4	147.0 MB
031 Constructor in Python.mp4	127.9 MB
006 Access elements of String.mp4	118.2 MB
030 Class and Objects Continued.mp4	107.6 MB
023 Important List Comprehension for Game Development.mp4	101.0 MB
022 For loop.mp4	97.6 MB
015 if else statements.mp4	93.3 MB
025 Learn to create Functions.mp4	83.1 MB
004 Operations on Numbers.mp4	80.3 MB
020 Infinite while loop (Game Loop).mp4	76.2 MB
018 How to access the items from the list.mp4	74.1 MB
029 Class and Objects.mp4	66.9 MB
017 Checking type of Data Structures.mp4	64.7 MB
007 Formatting strings.mp4	63.9 MB
009 Create Variables in Python.mp4	62.8 MB
026 Learn about return statements.mp4	61.1 MB
011 Learn to create conditions.mp4	50.6 MB
027 Introduction to the section.mp4	43.5 MB
028 What is Object Oriented Programming.mp4	39.8 MB
021 Finite Game Loop.mp4	38.1 MB
016 Introduction to Data Structures.mp4	36.4 MB
005 Introduction to Strings in Python.mp4	27.8 MB
002 Introduction to the data types.mp4	26.3 MB
001 What is Python.mp4	23.7 MB
010 Introduction to Booleans in Python.mp4	20.7 MB
014 Introduction to conditional statements.mp4	18.3 MB
008 Introduction to the variables.mp4	14.9 MB
012 is operator in Python.mp4	14.4 MB
024 What is Function and Why we need it.mp4	12.8 MB
019 Introduction to the loops in Python.mp4	12.1 MB
003 Basic Arithmetic in Python_en.vtt	24.9 KB
013 Logical statements_en.vtt	23.2 KB
023 Important List Comprehension for Game Development_en.vtt	19.3 KB
033 Multiple Inheritance_en.vtt	18.9 KB
022 For loop_en.vtt	17.4 KB
020 Infinite while loop (Game Loop)_en.vtt	14.8 KB
004 Operations on Numbers_en.vtt	14.6 KB
032 What is Inheritance_en.vtt	14.5 KB
031 Constructor in Python_en.vtt	14.0 KB
030 Class and Objects Continued_en.vtt	13.8 KB
007 Formatting strings_en.vtt	13.6 KB
025 Learn to create Functions_en.vtt	12.4 KB
006 Access elements of String_en.vtt	12.1 KB
018 How to access the items from the list_en.vtt	11.2 KB
009 Create Variables in Python_en.vtt	11.1 KB
015 if else statements_en.vtt	11.0 KB
017 Checking type of Data Structures_en.vtt	10.3 KB
029 Class and Objects_en.vtt	9.5 KB
026 Learn about return statements_en.vtt	9.1 KB
011 Learn to create conditions_en.vtt	7.0 KB
021 Finite Game Loop_en.vtt	6.5 KB
005 Introduction to Strings in Python_en.vtt	5.0 KB
028 What is Object Oriented Programming_en.vtt	4.9 KB
027 Introduction to the section_en.vtt	3.2 KB
012 is operator in Python_en.vtt	2.9 KB
016 Introduction to Data Structures_en.vtt	2.8 KB
001 What is Python_en.vtt	2.0 KB
002 Introduction to the data types_en.vtt	1.4 KB
010 Introduction to Booleans in Python_en.vtt	1.4 KB
008 Introduction to the variables_en.vtt	1.0 KB
014 Introduction to conditional statements_en.vtt	0.9 KB
024 What is Function and Why we need it_en.vtt	0.9 KB
019 Introduction to the loops in Python_en.vtt	0.7 KB
04 - Pygame Refresher/
002 Pygame coordinate System.mp4	138.0 MB
003 Introduction to Pygame shape.mp4	97.3 MB
006 Fundamentals of Pygame -- skeleton code.mp4	93.2 MB
004 Draw shapes using Pygame.mp4	87.0 MB
010 Make movement within Boundary.mp4	82.5 MB
005 Color Picker.mp4	73.2 MB
001 Introduction to the pygame.mp4	53.8 MB
008 Movement of the shapes.mp4	52.7 MB
007 Render a rectangle in the Screen.mp4	52.1 MB
009 Smoothen the movement using FPS.mp4	40.2 MB
002 Pygame coordinate System_en.vtt	20.7 KB
010 Make movement within Boundary_en.vtt	13.9 KB
006 Fundamentals of Pygame -- skeleton code_en.vtt	12.7 KB
004 Draw shapes using Pygame_en.vtt	10.6 KB
008 Movement of the shapes_en.vtt	8.3 KB
007 Render a rectangle in the Screen_en.vtt	7.6 KB
003 Introduction to Pygame shape_en.vtt	7.4 KB
005 Color Picker_en.vtt	7.1 KB
009 Smoothen the movement using FPS_en.vtt	6.5 KB
001 Introduction to the pygame_en.vtt	6.5 KB
05 - Introduction to MinMax Algorithm/
006 Example of Heuristic.mp4	71.2 MB
008 Example of MinMax.mp4	67.8 MB
007 Introduction to MinMax algorithm.mp4	63.2 MB
001 Introduction to Board Games.mp4	59.8 MB
004 Solution of Lookahead problem.mp4	55.9 MB
002 Tree representation of Game.mp4	48.4 MB
009 MinMax Example for TicTacToe.mp4	42.5 MB
005 Heuristic Evaluation of Board.mp4	38.7 MB
003 Lookahead Problem.mp4	31.7 MB
010 MinMax Algorithm.mp4	9.1 MB
007 Introduction to MinMax algorithm_en.vtt	17.7 KB
006 Example of Heuristic_en.vtt	15.0 KB
008 Example of MinMax_en.vtt	14.7 KB
001 Introduction to Board Games_en.vtt	14.5 KB
004 Solution of Lookahead problem_en.vtt	11.8 KB
002 Tree representation of Game_en.vtt	10.5 KB
009 MinMax Example for TicTacToe_en.vtt	9.7 KB
005 Heuristic Evaluation of Board_en.vtt	9.0 KB
003 Lookahead Problem_en.vtt	8.4 KB
010 MinMax Algorithm_en.vtt	2.1 KB
06 - Creating TicTacToe using MinMax algorithm/
006 Implementing MinMax algorithm.mp4	118.6 MB
008 Playing against AI player and Tuning algorithm.mp4	100.4 MB
005 Calculating ValueHeuristic for Min Max player.mp4	75.8 MB
002 Introduction to Project Files.mp4	70.4 MB
003 Creating Indecisive Player (Random).mp4	62.4 MB
007 Setting up Autoplayer (Artificial Intelligent Player).mp4	55.6 MB
004 Implementing MinMax.mp4	50.0 MB
001 introduction to Game.mp4	46.6 MB
006 Implementing MinMax algorithm_en.vtt	19.8 KB
008 Playing against AI player and Tuning algorithm_en.vtt	12.7 KB
003 Creating Indecisive Player (Random)_en.vtt	12.5 KB
005 Calculating ValueHeuristic for Min Max player_en.vtt	11.5 KB
002 Introduction to Project Files_en.vtt	10.8 KB
004 Implementing MinMax_en.vtt	8.7 KB
001 introduction to Game_en.vtt	8.0 KB
007 Setting up Autoplayer (Artificial Intelligent Player)_en.vtt	7.5 KB
07 - Introduction to Artificial Intelligence/
009 Value of the State.mp4	58.2 MB
002 Reinforcement Learning.mp4	51.4 MB
001 Motivation for Artificial Intelligence.mp4	22.6 MB
007 Policy.mp4	19.9 MB
010 Model.mp4	19.1 MB
006 Typical RL scenario.mp4	18.4 MB
004 Rewards.mp4	13.9 MB
003 Environment.mp4	12.0 MB
005 Path.mp4	5.7 MB
008 Rewards.mp4	5.6 MB
009 Value of the State_en.vtt	8.4 KB
010 Model_en.vtt	5.0 KB
006 Typical RL scenario_en.vtt	4.0 KB
007 Policy_en.vtt	3.5 KB
001 Motivation for Artificial Intelligence_en.vtt	3.0 KB
004 Rewards_en.vtt	2.8 KB
002 Reinforcement Learning_en.vtt	2.8 KB
003 Environment_en.vtt	2.7 KB
005 Path_en.vtt	1.2 KB
008 Rewards_en.vtt	0.9 KB
08 - Key Terms of Artificial Intelligence (Important)/
003 Markov Decision Process.mp4	76.5 MB
001 Markov Property and Markov Chain.mp4	41.1 MB
002 Markov Reward Process.mp4	30.1 MB
003 Markov Decision Process_en.vtt	10.7 KB
001 Markov Property and Markov Chain_en.vtt	8.4 KB
002 Markov Reward Process_en.vtt	3.7 KB
09 - Bellman Equation and Dynamic Programming/
012 Temporal Difference.mp4	140.5 MB
008 Markov Decision Process + Bellman.mp4	85.4 MB
010 Equation of Q-Learning.mp4	78.0 MB
005 Example.mp4	60.1 MB
006 Plan.mp4	60.0 MB
004 Bellman Equation.mp4	55.6 MB
007 Non Deterministic Environment.mp4	51.7 MB
009 Introduction to Q-Learning.mp4	47.7 MB
003 Value Function.mp4	33.1 MB
001 Introduction.mp4	18.2 MB
002 Tribute to Bellman.mp4	15.3 MB
011 Q value for Non-Deterministic Environment.mp4	12.1 MB
012 Temporal Difference_en.vtt	35.9 KB
008 Markov Decision Process + Bellman_en.vtt	19.7 KB
010 Equation of Q-Learning_en.vtt	16.9 KB
004 Bellman Equation_en.vtt	15.5 KB
005 Example_en.vtt	14.3 KB
007 Non Deterministic Environment_en.vtt	11.9 KB
009 Introduction to Q-Learning_en.vtt	10.8 KB
006 Plan_en.vtt	9.7 KB
003 Value Function_en.vtt	7.8 KB
001 Introduction_en.vtt	4.8 KB
002 Tribute to Bellman_en.vtt	4.1 KB
011 Q value for Non-Deterministic Environment_en.vtt	3.0 KB
10 - Implementation of Q-Learning to Find Optimal Path/
002 Introduction to Project Files.mp4	112.6 MB
011 Executing Gameq-Learning Algorithm.mp4	99.7 MB
010 Implementing Temporal Difference.mp4	78.7 MB
001 Introduction to Project.mp4	78.4 MB
005 Example of Q-Table.mp4	76.3 MB
004 Briefing about Q-Table.mp4	70.8 MB
009 Action Selection Policy (Returning max Q value).mp4	70.0 MB
006 Q-Agent.mp4	60.2 MB
003 Creating Environment.mp4	55.4 MB
007 Possible Actions.mp4	39.6 MB
008 Iterations.mp4	33.7 MB
002 Introduction to Project Files_en.vtt	15.9 KB
011 Executing Gameq-Learning Algorithm_en.vtt	14.0 KB
005 Example of Q-Table_en.vtt	13.7 KB
009 Action Selection Policy (Returning max Q value)_en.vtt	13.3 KB
006 Q-Agent_en.vtt	12.0 KB
001 Introduction to Project_en.vtt	11.5 KB
010 Implementing Temporal Difference_en.vtt	10.3 KB
003 Creating Environment_en.vtt	9.3 KB
004 Briefing about Q-Table_en.vtt	8.2 KB
007 Possible Actions_en.vtt	7.8 KB
008 Iterations_en.vtt	5.4 KB
11 - Introduction to gym module/
008 Tennis Game with Random Policy.mp4	214.2 MB
006 Transitional Probability.mp4	180.6 MB
007 CartPole Example.mp4	155.7 MB
009 CartPole with Random Policy.mp4	80.9 MB
003 Creating Gym Environment.mp4	72.0 MB
005 State space and Action space.mp4	51.0 MB
002 Example of Gym Environment.mp4	49.4 MB
004 Getting started with Gym.mp4	47.4 MB
001 The gym module.mp4	24.7 MB
006 Transitional Probability_en.vtt	33.4 KB
008 Tennis Game with Random Policy_en.vtt	30.5 KB
007 CartPole Example_en.vtt	19.6 KB
003 Creating Gym Environment_en.vtt	12.6 KB
009 CartPole with Random Policy_en.vtt	11.4 KB
005 State space and Action space_en.vtt	8.7 KB
004 Getting started with Gym_en.vtt	8.4 KB
002 Example of Gym Environment_en.vtt	8.4 KB
001 The gym module_en.vtt	3.2 KB
12 - Monte Carlo Simulation/
003 Monte Carlo Method (MC - method).mp4	76.8 MB
001 Why Monte Carlo Simulation is important.mp4	67.8 MB
004 First Visit vs Every Visit MC.mp4	41.8 MB
002 Monte Carlo Simulation.mp4	28.1 MB
005 BlackJack Example.mp4	23.0 MB
003 Monte Carlo Method (MC - method)_en.vtt	12.7 KB
001 Why Monte Carlo Simulation is important_en.vtt	11.9 KB
004 First Visit vs Every Visit MC_en.vtt	7.5 KB
002 Monte Carlo Simulation_en.vtt	6.1 KB
005 BlackJack Example_en.vtt	4.0 KB
13 - Implementing Monte Carlo Predictions/
005 Implementing MC simulation.mp4	136.0 MB
001 BlackJack Game and Rules of the Game.mp4	125.4 MB
006 Calculate Value of State using MC simulation.mp4	117.3 MB
003 Defining Policy.mp4	89.8 MB
004 Generating Episodes.mp4	79.5 MB
002 Creating BlackJack Environment.mp4	49.4 MB
005 Implementing MC simulation_en.vtt	22.0 KB
001 BlackJack Game and Rules of the Game_en.vtt	18.8 KB
006 Calculate Value of State using MC simulation_en.vtt	15.5 KB
003 Defining Policy_en.vtt	13.5 KB
004 Generating Episodes_en.vtt	12.0 KB
002 Creating BlackJack Environment_en.vtt	8.3 KB
14 - Creating BlackJack Game/
002 Introduction to Project Files.mp4	147.9 MB
010 Training the Q-Learning model and Running Game.mp4	113.5 MB
007 Implementing Temporal Difference (update Q-values).mp4	110.2 MB
009 Making AI to play game.mp4	82.2 MB
008 AI Player steps.mp4	76.2 MB
005 (State, Action, Reward) of Episodes.mp4	63.1 MB
001 Action Selection Policy (Epsilon-Greedy).mp4	47.1 MB
004 Implementing Epsilon Greedy Policy.mp4	45.1 MB
006 Introduction to Discount Parameter.mp4	42.0 MB
003 Q-Table.mp4	31.6 MB
002 Introduction to Project Files_en.vtt	20.2 KB
007 Implementing Temporal Difference (update Q-values)_en.vtt	15.8 KB
010 Training the Q-Learning model and Running Game_en.vtt	15.6 KB
008 AI Player steps_en.vtt	10.4 KB
005 (State, Action, Reward) of Episodes_en.vtt	10.2 KB
009 Making AI to play game_en.vtt	10.2 KB
004 Implementing Epsilon Greedy Policy_en.vtt	9.8 KB
001 Action Selection Policy (Epsilon-Greedy)_en.vtt	8.4 KB
006 Introduction to Discount Parameter_en.vtt	7.0 KB
003 Q-Table_en.vtt	3.8 KB
15 - Neural Network Refresher/
002 Introduction to Neural Networks.mp4	122.0 MB
008 Introduction to the Activation Function.mp4	110.7 MB
006 Updating the weights [partial differentiation].mp4	97.0 MB
012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp4	81.7 MB
003 Inspiration and representation for Neural Network.mp4	80.8 MB
004 History and Application of Neural Network.mp4	72.9 MB
001 Introduction to Artificial Intelligence.mp4	71.6 MB
010 Why we use regularization in the Neural Network.mp4	66.2 MB
011 Introduction to the gradient descent [review].mp4	63.4 MB
007 Introduction to partial differentiation.mp4	58.8 MB
005 Example of neural network.mp4	51.7 MB
009 Why do we need bias in the program.mp4	46.9 MB
013 Introduction to mini-batch SGD.mp4	17.0 MB
002 Introduction to Neural Networks_en.vtt	30.1 KB
008 Introduction to the Activation Function_en.vtt	29.5 KB
006 Updating the weights [partial differentiation]_en.vtt	22.7 KB
012 Introduction to Stochastic Gradient Descent and Adam Optimizer_en.vtt	21.7 KB
007 Introduction to partial differentiation_en.vtt	19.6 KB
011 Introduction to the gradient descent [review]_en.vtt	19.6 KB
004 History and Application of Neural Network_en.vtt	17.8 KB
003 Inspiration and representation for Neural Network_en.vtt	17.1 KB
005 Example of neural network_en.vtt	16.4 KB
001 Introduction to Artificial Intelligence_en.vtt	12.1 KB
009 Why do we need bias in the program_en.vtt	11.7 KB
010 Why we use regularization in the Neural Network_en.vtt	3.7 KB
013 Introduction to mini-batch SGD_en.vtt	3.5 KB
16 - Scratch Implementation of Neural Network/
004 Coding dense layer [must know Object Oriented Programming].mp4	127.2 MB
005 Introduction to Activation Function.mp4	110.0 MB
002 Coding neuron layer.mp4	99.2 MB
006 Implementation of activation function [step and sigmoid].mp4	72.6 MB
001 Setting up environment and coding single neuron.mp4	72.1 MB
007 Implementation of activation function [tanh and ReLu].mp4	65.0 MB
003 Using dot product to code neuron layer.mp4	51.5 MB
004 Coding dense layer [must know Object Oriented Programming]_en.vtt	24.7 KB
002 Coding neuron layer_en.vtt	21.6 KB
001 Setting up environment and coding single neuron_en.vtt	19.5 KB
005 Introduction to Activation Function_en.vtt	19.1 KB
007 Implementation of activation function [tanh and ReLu]_en.vtt	16.7 KB
006 Implementation of activation function [step and sigmoid]_en.vtt	15.3 KB
003 Using dot product to code neuron layer_en.vtt	12.7 KB
17 - Tensorflow and Keras/
006 Keras models (Important).mp4	128.5 MB
004 Examples.mp4	102.8 MB
001 What is Tensorflow.mp4	63.6 MB
002 Rank of Tensors.mp4	58.4 MB
007 Implementing Neural Network using Keras.mp4	49.1 MB
003 Program Elements of Tensorflow.mp4	46.1 MB
005 Introduction to Keras.mp4	43.4 MB
006 Keras models (Important)_en.vtt	13.1 KB
004 Examples_en.vtt	12.1 KB
002 Rank of Tensors_en.vtt	10.8 KB
001 What is Tensorflow_en.vtt	9.9 KB
003 Program Elements of Tensorflow_en.vtt	9.0 KB
007 Implementing Neural Network using Keras_en.vtt	5.6 KB
005 Introduction to Keras_en.vtt	5.4 KB
18 - TicTacToe Tensorflow/
003 Preprocess the state.mp4	186.9 MB
009 Creating Neural Network Player.mp4	180.1 MB
002 Creating model for the Game.mp4	149.1 MB
008 TicTacToe Neural Network.mp4	147.0 MB
005 Training the model.mp4	61.0 MB
004 Define Independent (input) and Dependent (output) Variable.mp4	47.6 MB
001 Introduction to Project Files.mp4	44.8 MB
007 TicTacToe Model.mp4	31.7 MB
006 Predict from the model.mp4	12.0 MB
003 Preprocess the state_en.vtt	32.9 KB
002 Creating model for the Game_en.vtt	27.1 KB
009 Creating Neural Network Player_en.vtt	21.5 KB
008 TicTacToe Neural Network_en.vtt	21.2 KB
005 Training the model_en.vtt	9.8 KB
001 Introduction to Project Files_en.vtt	6.8 KB
004 Define Independent (input) and Dependent (output) Variable_en.vtt	6.4 KB
007 TicTacToe Model_en.vtt	5.5 KB
006 Predict from the model_en.vtt	1.6 KB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/
002 Action Selection Policy.mp4	109.1 MB
003 Exploration vs Exploitation.mp4	96.8 MB
001 Introduction to Deep Q-Learning.mp4	79.6 MB
004 Deep Convolution Q-Learning.mp4	42.0 MB
001 Introduction to Deep Q-Learning_en.vtt	16.1 KB
002 Action Selection Policy_en.vtt	12.6 KB
003 Exploration vs Exploitation_en.vtt	11.0 KB
004 Deep Convolution Q-Learning_en.vtt	9.4 KB
20 - Convolution Neural Network/
003 Convolution Layer.mp4	101.0 MB
006 BackPropagation.mp4	55.1 MB
001 Introduction to convolution neural network.mp4	32.5 MB
002 How ConvNet works.mp4	30.8 MB
004 RELU Layer.mp4	20.4 MB
005 Pooling Layer.mp4	17.3 MB
003 Convolution Layer_en.vtt	17.0 KB
006 BackPropagation_en.vtt	8.3 KB
001 Introduction to convolution neural network_en.vtt	5.3 KB
002 How ConvNet works_en.vtt	5.1 KB
004 RELU Layer_en.vtt	3.6 KB
005 Pooling Layer_en.vtt	3.4 KB
21 - Deep Convolution Q-Learning Practical Pacman game/
006 Build Convolution Neural Network.mp4	203.4 MB
013 Training model for multiple iterations.mp4	120.2 MB
002 Mean Squared Error.mp4	118.4 MB
001 Introduction to Replay Buffer.mp4	108.3 MB
005 Solving ROM error.mp4	106.7 MB
009 Epsilon Greedy (Action-Selection Policy).mp4	106.4 MB
014 Simulating the game and storing transitions.mp4	100.3 MB
010 Training the neural network.mp4	94.3 MB
008 Build Main Network and Target Network.mp4	88.5 MB
004 Creating Environment.mp4	81.6 MB
012 Preprocess the state.mp4	67.9 MB
003 Main Network and Target Network.mp4	49.9 MB
015 Testing the game.mp4	48.6 MB
011 Fit the model.mp4	40.8 MB
007 Store Transition in Replay buffer.mp4	37.9 MB
006 Build Convolution Neural Network_en.vtt	32.8 KB
002 Mean Squared Error_en.vtt	27.4 KB
001 Introduction to Replay Buffer_en.vtt	21.7 KB
013 Training model for multiple iterations_en.vtt	17.6 KB
010 Training the neural network_en.vtt	17.1 KB
009 Epsilon Greedy (Action-Selection Policy)_en.vtt	15.5 KB
014 Simulating the game and storing transitions_en.vtt	14.6 KB
008 Build Main Network and Target Network_en.vtt	13.6 KB
005 Solving ROM error_en.vtt	13.5 KB
003 Main Network and Target Network_en.vtt	11.7 KB
004 Creating Environment_en.vtt	10.7 KB
012 Preprocess the state_en.vtt	9.9 KB
011 Fit the model_en.vtt	6.3 KB
007 Store Transition in Replay buffer_en.vtt	5.4 KB
015 Testing the game_en.vtt	4.9 KB
22 - Any games you want to suggest/
001 Farewell.html	0.3 KB
Total files 387