Learn To Create Artificially Intelligent Games Using Python3

Creation Time	Nov. 11, 2024, 4:09 a.m.
Last Access Time	Jan. 13, 2026, 9:57 p.m.
File Size	12.6 GB
Keywords	Create To Games Artificially Learn Using Intelligent Python3
Total Requests	212
Total Files	387
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

11 - Introduction to gym module/008 Tennis Game with Random Policy.mp4 204.3 MB
21 - Deep Convolution Q-Learning Practical Pacman game/006 Build Convolution Neural Network.mp4 194.0 MB
18 - TicTacToe Tensorflow/003 Preprocess the state.mp4 178.2 MB
11 - Introduction to gym module/006 Transitional Probability.mp4 172.3 MB
18 - TicTacToe Tensorflow/009 Creating Neural Network Player.mp4 171.8 MB
03 - Python Essentials/013 Logical statements.mp4 169.5 MB
03 - Python Essentials/033 Multiple Inheritance.mp4 159.4 MB
11 - Introduction to gym module/007 CartPole Example.mp4 148.5 MB
18 - TicTacToe Tensorflow/002 Creating model for the Game.mp4 142.2 MB
14 - Creating BlackJack Game/002 Introduction to Project Files.mp4 141.1 MB
03 - Python Essentials/032 What is Inheritance.mp4 140.8 MB
18 - TicTacToe Tensorflow/008 TicTacToe Neural Network.mp4 140.2 MB
03 - Python Essentials/003 Basic Arithmetic in Python.mp4 140.2 MB
09 - Bellman Equation and Dynamic Programming/012 Temporal Difference.mp4 134.0 MB
04 - Pygame Refresher/002 Pygame coordinate System.mp4 131.6 MB
13 - Implementing Monte Carlo Predictions/005 Implementing MC simulation.mp4 129.7 MB
17 - Tensorflow and Keras/006 Keras models (Important).mp4 122.6 MB
03 - Python Essentials/031 Constructor in Python.mp4 122.0 MB
16 - Scratch Implementation of Neural Network/004 Coding dense layer [must know Object Oriented Programming].mp4 121.3 MB
13 - Implementing Monte Carlo Predictions/001 BlackJack Game and Rules of the Game.mp4 119.6 MB
15 - Neural Network Refresher/002 Introduction to Neural Networks.mp4 116.3 MB
21 - Deep Convolution Q-Learning Practical Pacman game/013 Training model for multiple iterations.mp4 114.6 MB
06 - Creating TicTacToe using MinMax algorithm/006 Implementing MinMax algorithm.mp4 113.1 MB
21 - Deep Convolution Q-Learning Practical Pacman game/002 Mean Squared Error.mp4 112.9 MB
03 - Python Essentials/006 Access elements of String.mp4 112.7 MB
13 - Implementing Monte Carlo Predictions/006 Calculate Value of State using MC simulation.mp4 111.9 MB
14 - Creating BlackJack Game/010 Training the Q-Learning model and Running Game.mp4 108.2 MB
10 - Implementation of Q-Learning to Find Optimal Path/002 Introduction to Project Files.mp4 107.4 MB
15 - Neural Network Refresher/008 Introduction to the Activation Function.mp4 105.6 MB
14 - Creating BlackJack Game/007 Implementing Temporal Difference (update Q-values).mp4 105.1 MB
16 - Scratch Implementation of Neural Network/005 Introduction to Activation Function.mp4 104.9 MB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/002 Action Selection Policy.mp4 104.1 MB
21 - Deep Convolution Q-Learning Practical Pacman game/001 Introduction to Replay Buffer.mp4 103.3 MB
03 - Python Essentials/030 Class and Objects Continued.mp4 102.6 MB
21 - Deep Convolution Q-Learning Practical Pacman game/005 Solving ROM error.mp4 101.7 MB
21 - Deep Convolution Q-Learning Practical Pacman game/009 Epsilon Greedy (Action-Selection Policy).mp4 101.5 MB
17 - Tensorflow and Keras/004 Examples.mp4 98.1 MB
20 - Convolution Neural Network/003 Convolution Layer.mp4 96.4 MB
03 - Python Essentials/023 Important List Comprehension for Game Development.mp4 96.3 MB
06 - Creating TicTacToe using MinMax algorithm/008 Playing against AI player and Tuning algorithm.mp4 95.8 MB
21 - Deep Convolution Q-Learning Practical Pacman game/014 Simulating the game and storing transitions.mp4 95.7 MB
10 - Implementation of Q-Learning to Find Optimal Path/011 Executing Gameq-Learning Algorithm.mp4 95.1 MB
16 - Scratch Implementation of Neural Network/002 Coding neuron layer.mp4 94.6 MB
03 - Python Essentials/022 For loop.mp4 93.1 MB
04 - Pygame Refresher/003 Introduction to Pygame shape.mp4 92.8 MB
15 - Neural Network Refresher/006 Updating the weights [partial differentiation].mp4 92.5 MB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/003 Exploration vs Exploitation.mp4 92.3 MB
21 - Deep Convolution Q-Learning Practical Pacman game/010 Training the neural network.mp4 89.9 MB
03 - Python Essentials/015 if else statements.mp4 89.0 MB
04 - Pygame Refresher/006 Fundamentals of Pygame -- skeleton code.mp4 88.9 MB
13 - Implementing Monte Carlo Predictions/003 Defining Policy.mp4 85.6 MB
21 - Deep Convolution Q-Learning Practical Pacman game/008 Build Main Network and Target Network.mp4 84.4 MB
02 - Setup Anaconda and Install Dependencies for Project/003 Install DependenciesLibraries for the Course.mp4 83.4 MB
04 - Pygame Refresher/004 Draw shapes using Pygame.mp4 82.9 MB
09 - Bellman Equation and Dynamic Programming/008 Markov Decision Process + Bellman.mp4 81.5 MB
03 - Python Essentials/025 Learn to create Functions.mp4 79.3 MB
04 - Pygame Refresher/010 Make movement within Boundary.mp4 78.7 MB
14 - Creating BlackJack Game/009 Making AI to play game.mp4 78.4 MB
15 - Neural Network Refresher/012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp4 77.9 MB
21 - Deep Convolution Q-Learning Practical Pacman game/004 Creating Environment.mp4 77.8 MB
11 - Introduction to gym module/009 CartPole with Random Policy.mp4 77.2 MB
15 - Neural Network Refresher/003 Inspiration and representation for Neural Network.mp4 77.0 MB
03 - Python Essentials/004 Operations on Numbers.mp4 76.5 MB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/001 Introduction to Deep Q-Learning.mp4 75.9 MB
13 - Implementing Monte Carlo Predictions/004 Generating Episodes.mp4 75.9 MB
10 - Implementation of Q-Learning to Find Optimal Path/010 Implementing Temporal Difference.mp4 75.1 MB
10 - Implementation of Q-Learning to Find Optimal Path/001 Introduction to Project.mp4 74.8 MB
09 - Bellman Equation and Dynamic Programming/010 Equation of Q-Learning.mp4 74.4 MB
12 - Monte Carlo Simulation/003 Monte Carlo Method (MC - method).mp4 73.2 MB
08 - Key Terms of Artificial Intelligence (Important)/003 Markov Decision Process.mp4 73.0 MB
10 - Implementation of Q-Learning to Find Optimal Path/005 Example of Q-Table.mp4 72.7 MB
14 - Creating BlackJack Game/008 AI Player steps.mp4 72.7 MB
03 - Python Essentials/020 Infinite while loop (Game Loop).mp4 72.6 MB
06 - Creating TicTacToe using MinMax algorithm/005 Calculating ValueHeuristic for Min Max player.mp4 72.3 MB
03 - Python Essentials/018 How to access the items from the list.mp4 70.7 MB
04 - Pygame Refresher/005 Color Picker.mp4 69.8 MB
15 - Neural Network Refresher/004 History and Application of Neural Network.mp4 69.5 MB
16 - Scratch Implementation of Neural Network/006 Implementation of activation function [step and sigmoid].mp4 69.3 MB
16 - Scratch Implementation of Neural Network/001 Setting up environment and coding single neuron.mp4 68.8 MB
11 - Introduction to gym module/003 Creating Gym Environment.mp4 68.6 MB
15 - Neural Network Refresher/001 Introduction to Artificial Intelligence.mp4 68.3 MB
05 - Introduction to MinMax Algorithm/006 Example of Heuristic.mp4 67.9 MB
10 - Implementation of Q-Learning to Find Optimal Path/004 Briefing about Q-Table.mp4 67.5 MB
06 - Creating TicTacToe using MinMax algorithm/002 Introduction to Project Files.mp4 67.2 MB
10 - Implementation of Q-Learning to Find Optimal Path/009 Action Selection Policy (Returning max Q value).mp4 66.8 MB
21 - Deep Convolution Q-Learning Practical Pacman game/012 Preprocess the state.mp4 64.7 MB
05 - Introduction to MinMax Algorithm/008 Example of MinMax.mp4 64.7 MB
12 - Monte Carlo Simulation/001 Why Monte Carlo Simulation is important.mp4 64.7 MB
02 - Setup Anaconda and Install Dependencies for Project/001 Install Anaconda.mp4 64.5 MB
03 - Python Essentials/029 Class and Objects.mp4 63.8 MB
15 - Neural Network Refresher/010 Why we use regularization in the Neural Network.mp4 63.2 MB
16 - Scratch Implementation of Neural Network/007 Implementation of activation function [tanh and ReLu].mp4 62.0 MB
03 - Python Essentials/017 Checking type of Data Structures.mp4 61.7 MB
03 - Python Essentials/007 Formatting strings.mp4 60.9 MB
17 - Tensorflow and Keras/001 What is Tensorflow.mp4 60.6 MB
15 - Neural Network Refresher/011 Introduction to the gradient descent [review].mp4 60.5 MB
05 - Introduction to MinMax Algorithm/007 Introduction to MinMax algorithm.mp4 60.3 MB
14 - Creating BlackJack Game/005 (State, Action, Reward) of Episodes.mp4 60.2 MB
03 - Python Essentials/009 Create Variables in Python.mp4 59.9 MB
06 - Creating TicTacToe using MinMax algorithm/003 Creating Indecisive Player (Random).mp4 59.5 MB
03 - Python Essentials/026 Learn about return statements.mp4 58.3 MB
18 - TicTacToe Tensorflow/005 Training the model.mp4 58.2 MB
10 - Implementation of Q-Learning to Find Optimal Path/006 Q-Agent.mp4 57.4 MB
09 - Bellman Equation and Dynamic Programming/005 Example.mp4 57.3 MB
09 - Bellman Equation and Dynamic Programming/006 Plan.mp4 57.3 MB
05 - Introduction to MinMax Algorithm/001 Introduction to Board Games.mp4 57.1 MB
15 - Neural Network Refresher/007 Introduction to partial differentiation.mp4 56.1 MB
17 - Tensorflow and Keras/002 Rank of Tensors.mp4 55.7 MB
07 - Introduction to Artificial Intelligence/009 Value of the State.mp4 55.5 MB
05 - Introduction to MinMax Algorithm/004 Solution of Lookahead problem.mp4 53.3 MB
09 - Bellman Equation and Dynamic Programming/004 Bellman Equation.mp4 53.0 MB
06 - Creating TicTacToe using MinMax algorithm/007 Setting up Autoplayer (Artificial Intelligent Player).mp4 53.0 MB
10 - Implementation of Q-Learning to Find Optimal Path/003 Creating Environment.mp4 52.8 MB
20 - Convolution Neural Network/006 BackPropagation.mp4 52.6 MB
04 - Pygame Refresher/001 Introduction to the pygame.mp4 51.3 MB
04 - Pygame Refresher/008 Movement of the shapes.mp4 50.2 MB
04 - Pygame Refresher/007 Render a rectangle in the Screen.mp4 49.7 MB
15 - Neural Network Refresher/005 Example of neural network.mp4 49.4 MB
09 - Bellman Equation and Dynamic Programming/007 Non Deterministic Environment.mp4 49.3 MB
16 - Scratch Implementation of Neural Network/003 Using dot product to code neuron layer.mp4 49.1 MB
07 - Introduction to Artificial Intelligence/002 Reinforcement Learning.mp4 49.0 MB
11 - Introduction to gym module/005 State space and Action space.mp4 48.6 MB
03 - Python Essentials/011 Learn to create conditions.mp4 48.2 MB
06 - Creating TicTacToe using MinMax algorithm/004 Implementing MinMax.mp4 47.7 MB
21 - Deep Convolution Q-Learning Practical Pacman game/003 Main Network and Target Network.mp4 47.6 MB
13 - Implementing Monte Carlo Predictions/002 Creating BlackJack Environment.mp4 47.1 MB
11 - Introduction to gym module/002 Example of Gym Environment.mp4 47.1 MB
17 - Tensorflow and Keras/007 Implementing Neural Network using Keras.mp4 46.8 MB
21 - Deep Convolution Q-Learning Practical Pacman game/015 Testing the game.mp4 46.4 MB
05 - Introduction to MinMax Algorithm/002 Tree representation of Game.mp4 46.2 MB
09 - Bellman Equation and Dynamic Programming/009 Introduction to Q-Learning.mp4 45.5 MB
18 - TicTacToe Tensorflow/004 Define Independent (input) and Dependent (output) Variable.mp4 45.4 MB
11 - Introduction to gym module/004 Getting started with Gym.mp4 45.2 MB
14 - Creating BlackJack Game/001 Action Selection Policy (Epsilon-Greedy).mp4 45.0 MB
15 - Neural Network Refresher/009 Why do we need bias in the program.mp4 44.8 MB
06 - Creating TicTacToe using MinMax algorithm/001 introduction to Game.mp4 44.5 MB
17 - Tensorflow and Keras/003 Program Elements of Tensorflow.mp4 43.9 MB
14 - Creating BlackJack Game/004 Implementing Epsilon Greedy Policy.mp4 43.0 MB
18 - TicTacToe Tensorflow/001 Introduction to Project Files.mp4 42.7 MB
03 - Python Essentials/027 Introduction to the section.mp4 41.5 MB
17 - Tensorflow and Keras/005 Introduction to Keras.mp4 41.4 MB
02 - Setup Anaconda and Install Dependencies for Project/004 Download Visual Studio Code.mp4 41.2 MB
05 - Introduction to MinMax Algorithm/009 MinMax Example for TicTacToe.mp4 40.6 MB
14 - Creating BlackJack Game/006 Introduction to Discount Parameter.mp4 40.1 MB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/004 Deep Convolution Q-Learning.mp4 40.0 MB
12 - Monte Carlo Simulation/004 First Visit vs Every Visit MC.mp4 39.9 MB
02 - Setup Anaconda and Install Dependencies for Project/002 Create Virtual Environment.mp4 39.6 MB
08 - Key Terms of Artificial Intelligence (Important)/001 Markov Property and Markov Chain.mp4 39.2 MB
21 - Deep Convolution Q-Learning Practical Pacman game/011 Fit the model.mp4 38.9 MB
04 - Pygame Refresher/009 Smoothen the movement using FPS.mp4 38.3 MB
03 - Python Essentials/028 What is Object Oriented Programming.mp4 38.0 MB
10 - Implementation of Q-Learning to Find Optimal Path/007 Possible Actions.mp4 37.8 MB
05 - Introduction to MinMax Algorithm/005 Heuristic Evaluation of Board.mp4 36.9 MB
03 - Python Essentials/021 Finite Game Loop.mp4 36.3 MB
21 - Deep Convolution Q-Learning Practical Pacman game/007 Store Transition in Replay buffer.mp4 36.1 MB
03 - Python Essentials/016 Introduction to Data Structures.mp4 34.8 MB
01 - Introduction/001 Introduction.mp4 33.8 MB
10 - Implementation of Q-Learning to Find Optimal Path/008 Iterations.mp4 32.2 MB
09 - Bellman Equation and Dynamic Programming/003 Value Function.mp4 31.6 MB
20 - Convolution Neural Network/001 Introduction to convolution neural network.mp4 31.0 MB
18 - TicTacToe Tensorflow/007 TicTacToe Model.mp4 30.3 MB
05 - Introduction to MinMax Algorithm/003 Lookahead Problem.mp4 30.2 MB
14 - Creating BlackJack Game/003 Q-Table.mp4 30.1 MB
20 - Convolution Neural Network/002 How ConvNet works.mp4 29.3 MB
08 - Key Terms of Artificial Intelligence (Important)/002 Markov Reward Process.mp4 28.7 MB
12 - Monte Carlo Simulation/002 Monte Carlo Simulation.mp4 26.8 MB
03 - Python Essentials/005 Introduction to Strings in Python.mp4 26.5 MB
03 - Python Essentials/002 Introduction to the data types.mp4 25.1 MB
11 - Introduction to gym module/001 The gym module.mp4 23.6 MB
03 - Python Essentials/001 What is Python.mp4 22.6 MB
12 - Monte Carlo Simulation/005 BlackJack Example.mp4 21.9 MB
07 - Introduction to Artificial Intelligence/001 Motivation for Artificial Intelligence.mp4 21.6 MB
03 - Python Essentials/010 Introduction to Booleans in Python.mp4 19.8 MB
20 - Convolution Neural Network/004 RELU Layer.mp4 19.5 MB
07 - Introduction to Artificial Intelligence/007 Policy.mp4 19.0 MB
07 - Introduction to Artificial Intelligence/010 Model.mp4 18.2 MB
07 - Introduction to Artificial Intelligence/006 Typical RL scenario.mp4 17.5 MB
03 - Python Essentials/014 Introduction to conditional statements.mp4 17.4 MB
09 - Bellman Equation and Dynamic Programming/001 Introduction.mp4 17.4 MB
20 - Convolution Neural Network/005 Pooling Layer.mp4 16.5 MB
15 - Neural Network Refresher/013 Introduction to mini-batch SGD.mp4 16.2 MB
09 - Bellman Equation and Dynamic Programming/002 Tribute to Bellman.mp4 14.6 MB
03 - Python Essentials/008 Introduction to the variables.mp4 14.2 MB
03 - Python Essentials/012 is operator in Python.mp4 13.7 MB
07 - Introduction to Artificial Intelligence/004 Rewards.mp4 13.3 MB
03 - Python Essentials/024 What is Function and Why we need it.mp4 12.2 MB
03 - Python Essentials/019 Introduction to the loops in Python.mp4 11.5 MB
09 - Bellman Equation and Dynamic Programming/011 Q value for Non-Deterministic Environment.mp4 11.5 MB
18 - TicTacToe Tensorflow/006 Predict from the model.mp4 11.5 MB
07 - Introduction to Artificial Intelligence/003 Environment.mp4 11.4 MB
05 - Introduction to MinMax Algorithm/010 MinMax Algorithm.mp4 8.6 MB
07 - Introduction to Artificial Intelligence/005 Path.mp4 5.4 MB
07 - Introduction to Artificial Intelligence/008 Rewards.mp4 5.3 MB
09 - Bellman Equation and Dynamic Programming/012 Temporal Difference_en.vtt 35.0 KB
11 - Introduction to gym module/006 Transitional Probability_en.vtt 32.6 KB
18 - TicTacToe Tensorflow/003 Preprocess the state_en.vtt 32.2 KB
21 - Deep Convolution Q-Learning Practical Pacman game/006 Build Convolution Neural Network_en.vtt 32.0 KB
11 - Introduction to gym module/008 Tennis Game with Random Policy_en.vtt 29.8 KB
15 - Neural Network Refresher/002 Introduction to Neural Networks_en.vtt 29.4 KB
15 - Neural Network Refresher/008 Introduction to the Activation Function_en.vtt 28.8 KB
21 - Deep Convolution Q-Learning Practical Pacman game/002 Mean Squared Error_en.vtt 26.8 KB
18 - TicTacToe Tensorflow/002 Creating model for the Game_en.vtt 26.5 KB
03 - Python Essentials/003 Basic Arithmetic in Python_en.vtt 24.3 KB
16 - Scratch Implementation of Neural Network/004 Coding dense layer [must know Object Oriented Programming]_en.vtt 24.2 KB
03 - Python Essentials/013 Logical statements_en.vtt 22.7 KB
15 - Neural Network Refresher/006 Updating the weights [partial differentiation]_en.vtt 22.2 KB
13 - Implementing Monte Carlo Predictions/005 Implementing MC simulation_en.vtt 21.5 KB
21 - Deep Convolution Q-Learning Practical Pacman game/001 Introduction to Replay Buffer_en.vtt 21.2 KB
15 - Neural Network Refresher/012 Introduction to Stochastic Gradient Descent and Adam Optimizer_en.vtt 21.2 KB
16 - Scratch Implementation of Neural Network/002 Coding neuron layer_en.vtt 21.1 KB
18 - TicTacToe Tensorflow/009 Creating Neural Network Player_en.vtt 21.0 KB
18 - TicTacToe Tensorflow/008 TicTacToe Neural Network_en.vtt 20.7 KB
04 - Pygame Refresher/002 Pygame coordinate System_en.vtt 20.2 KB
14 - Creating BlackJack Game/002 Introduction to Project Files_en.vtt 19.8 KB
06 - Creating TicTacToe using MinMax algorithm/006 Implementing MinMax algorithm_en.vtt 19.3 KB
09 - Bellman Equation and Dynamic Programming/008 Markov Decision Process + Bellman_en.vtt 19.2 KB
11 - Introduction to gym module/007 CartPole Example_en.vtt 19.2 KB
15 - Neural Network Refresher/007 Introduction to partial differentiation_en.vtt 19.2 KB
15 - Neural Network Refresher/011 Introduction to the gradient descent [review]_en.vtt 19.1 KB
16 - Scratch Implementation of Neural Network/001 Setting up environment and coding single neuron_en.vtt 19.0 KB
03 - Python Essentials/023 Important List Comprehension for Game Development_en.vtt 18.9 KB
16 - Scratch Implementation of Neural Network/005 Introduction to Activation Function_en.vtt 18.6 KB
03 - Python Essentials/033 Multiple Inheritance_en.vtt 18.5 KB
13 - Implementing Monte Carlo Predictions/001 BlackJack Game and Rules of the Game_en.vtt 18.3 KB
15 - Neural Network Refresher/004 History and Application of Neural Network_en.vtt 17.4 KB
05 - Introduction to MinMax Algorithm/007 Introduction to MinMax algorithm_en.vtt 17.3 KB
21 - Deep Convolution Q-Learning Practical Pacman game/013 Training model for multiple iterations_en.vtt 17.2 KB
03 - Python Essentials/022 For loop_en.vtt 17.0 KB
15 - Neural Network Refresher/003 Inspiration and representation for Neural Network_en.vtt 16.7 KB
21 - Deep Convolution Q-Learning Practical Pacman game/010 Training the neural network_en.vtt 16.7 KB
20 - Convolution Neural Network/003 Convolution Layer_en.vtt 16.6 KB
09 - Bellman Equation and Dynamic Programming/010 Equation of Q-Learning_en.vtt 16.5 KB
16 - Scratch Implementation of Neural Network/007 Implementation of activation function [tanh and ReLu]_en.vtt 16.3 KB
15 - Neural Network Refresher/005 Example of neural network_en.vtt 16.0 KB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/001 Introduction to Deep Q-Learning_en.vtt 15.7 KB
10 - Implementation of Q-Learning to Find Optimal Path/002 Introduction to Project Files_en.vtt 15.6 KB
14 - Creating BlackJack Game/007 Implementing Temporal Difference (update Q-values)_en.vtt 15.4 KB
14 - Creating BlackJack Game/010 Training the Q-Learning model and Running Game_en.vtt 15.3 KB
13 - Implementing Monte Carlo Predictions/006 Calculate Value of State using MC simulation_en.vtt 15.2 KB
09 - Bellman Equation and Dynamic Programming/004 Bellman Equation_en.vtt 15.1 KB
21 - Deep Convolution Q-Learning Practical Pacman game/009 Epsilon Greedy (Action-Selection Policy)_en.vtt 15.1 KB
16 - Scratch Implementation of Neural Network/006 Implementation of activation function [step and sigmoid]_en.vtt 14.9 KB
05 - Introduction to MinMax Algorithm/006 Example of Heuristic_en.vtt 14.7 KB
03 - Python Essentials/020 Infinite while loop (Game Loop)_en.vtt 14.4 KB
05 - Introduction to MinMax Algorithm/008 Example of MinMax_en.vtt 14.3 KB
21 - Deep Convolution Q-Learning Practical Pacman game/014 Simulating the game and storing transitions_en.vtt 14.2 KB
03 - Python Essentials/004 Operations on Numbers_en.vtt 14.2 KB
03 - Python Essentials/032 What is Inheritance_en.vtt 14.2 KB
05 - Introduction to MinMax Algorithm/001 Introduction to Board Games_en.vtt 14.2 KB
09 - Bellman Equation and Dynamic Programming/005 Example_en.vtt 14.0 KB
10 - Implementation of Q-Learning to Find Optimal Path/011 Executing Gameq-Learning Algorithm_en.vtt 13.7 KB
03 - Python Essentials/031 Constructor in Python_en.vtt 13.6 KB
04 - Pygame Refresher/010 Make movement within Boundary_en.vtt 13.5 KB
03 - Python Essentials/030 Class and Objects Continued_en.vtt 13.5 KB
10 - Implementation of Q-Learning to Find Optimal Path/005 Example of Q-Table_en.vtt 13.3 KB
21 - Deep Convolution Q-Learning Practical Pacman game/008 Build Main Network and Target Network_en.vtt 13.3 KB
03 - Python Essentials/007 Formatting strings_en.vtt 13.3 KB
13 - Implementing Monte Carlo Predictions/003 Defining Policy_en.vtt 13.1 KB
21 - Deep Convolution Q-Learning Practical Pacman game/005 Solving ROM error_en.vtt 13.1 KB
10 - Implementation of Q-Learning to Find Optimal Path/009 Action Selection Policy (Returning max Q value)_en.vtt 13.0 KB
17 - Tensorflow and Keras/006 Keras models (Important)_en.vtt 12.8 KB
12 - Monte Carlo Simulation/003 Monte Carlo Method (MC - method)_en.vtt 12.4 KB
04 - Pygame Refresher/006 Fundamentals of Pygame -- skeleton code_en.vtt 12.4 KB
06 - Creating TicTacToe using MinMax algorithm/008 Playing against AI player and Tuning algorithm_en.vtt 12.4 KB
16 - Scratch Implementation of Neural Network/003 Using dot product to code neuron layer_en.vtt 12.4 KB
11 - Introduction to gym module/003 Creating Gym Environment_en.vtt 12.3 KB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/002 Action Selection Policy_en.vtt 12.3 KB
06 - Creating TicTacToe using MinMax algorithm/003 Creating Indecisive Player (Random)_en.vtt 12.2 KB
03 - Python Essentials/025 Learn to create Functions_en.vtt 12.2 KB
17 - Tensorflow and Keras/004 Examples_en.vtt 11.8 KB
15 - Neural Network Refresher/001 Introduction to Artificial Intelligence_en.vtt 11.8 KB
03 - Python Essentials/006 Access elements of String_en.vtt 11.8 KB
13 - Implementing Monte Carlo Predictions/004 Generating Episodes_en.vtt 11.8 KB
10 - Implementation of Q-Learning to Find Optimal Path/006 Q-Agent_en.vtt 11.7 KB
12 - Monte Carlo Simulation/001 Why Monte Carlo Simulation is important_en.vtt 11.6 KB
09 - Bellman Equation and Dynamic Programming/007 Non Deterministic Environment_en.vtt 11.6 KB
05 - Introduction to MinMax Algorithm/004 Solution of Lookahead problem_en.vtt 11.5 KB
15 - Neural Network Refresher/009 Why do we need bias in the program_en.vtt 11.5 KB
21 - Deep Convolution Q-Learning Practical Pacman game/003 Main Network and Target Network_en.vtt 11.4 KB
10 - Implementation of Q-Learning to Find Optimal Path/001 Introduction to Project_en.vtt 11.2 KB
06 - Creating TicTacToe using MinMax algorithm/005 Calculating ValueHeuristic for Min Max player_en.vtt 11.2 KB
11 - Introduction to gym module/009 CartPole with Random Policy_en.vtt 11.2 KB
03 - Python Essentials/018 How to access the items from the list_en.vtt 11.0 KB
03 - Python Essentials/009 Create Variables in Python_en.vtt 10.8 KB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/003 Exploration vs Exploitation_en.vtt 10.7 KB
03 - Python Essentials/015 if else statements_en.vtt 10.7 KB
17 - Tensorflow and Keras/002 Rank of Tensors_en.vtt 10.5 KB
06 - Creating TicTacToe using MinMax algorithm/002 Introduction to Project Files_en.vtt 10.5 KB
09 - Bellman Equation and Dynamic Programming/009 Introduction to Q-Learning_en.vtt 10.5 KB
08 - Key Terms of Artificial Intelligence (Important)/003 Markov Decision Process_en.vtt 10.5 KB
21 - Deep Convolution Q-Learning Practical Pacman game/004 Creating Environment_en.vtt 10.4 KB
04 - Pygame Refresher/004 Draw shapes using Pygame_en.vtt 10.3 KB
05 - Introduction to MinMax Algorithm/002 Tree representation of Game_en.vtt 10.2 KB
14 - Creating BlackJack Game/008 AI Player steps_en.vtt 10.1 KB
10 - Implementation of Q-Learning to Find Optimal Path/010 Implementing Temporal Difference_en.vtt 10.1 KB
03 - Python Essentials/017 Checking type of Data Structures_en.vtt 10.0 KB
14 - Creating BlackJack Game/005 (State, Action, Reward) of Episodes_en.vtt 10.0 KB
14 - Creating BlackJack Game/009 Making AI to play game_en.vtt 9.9 KB
21 - Deep Convolution Q-Learning Practical Pacman game/012 Preprocess the state_en.vtt 9.7 KB
17 - Tensorflow and Keras/001 What is Tensorflow_en.vtt 9.7 KB
14 - Creating BlackJack Game/004 Implementing Epsilon Greedy Policy_en.vtt 9.6 KB
18 - TicTacToe Tensorflow/005 Training the model_en.vtt 9.5 KB
05 - Introduction to MinMax Algorithm/009 MinMax Example for TicTacToe_en.vtt 9.5 KB
09 - Bellman Equation and Dynamic Programming/006 Plan_en.vtt 9.5 KB
03 - Python Essentials/029 Class and Objects_en.vtt 9.3 KB
19 - Introduction to Deep Q-Learning and Deep Convolution Q-Learning/004 Deep Convolution Q-Learning_en.vtt 9.1 KB
10 - Implementation of Q-Learning to Find Optimal Path/003 Creating Environment_en.vtt 9.1 KB
03 - Python Essentials/026 Learn about return statements_en.vtt 8.9 KB
05 - Introduction to MinMax Algorithm/005 Heuristic Evaluation of Board_en.vtt 8.8 KB
17 - Tensorflow and Keras/003 Program Elements of Tensorflow_en.vtt 8.8 KB
02 - Setup Anaconda and Install Dependencies for Project/004 Download Visual Studio Code_en.vtt 8.7 KB
11 - Introduction to gym module/005 State space and Action space_en.vtt 8.5 KB
06 - Creating TicTacToe using MinMax algorithm/004 Implementing MinMax_en.vtt 8.5 KB
02 - Setup Anaconda and Install Dependencies for Project/003 Install DependenciesLibraries for the Course_en.vtt 8.3 KB
08 - Key Terms of Artificial Intelligence (Important)/001 Markov Property and Markov Chain_en.vtt 8.2 KB
14 - Creating BlackJack Game/001 Action Selection Policy (Epsilon-Greedy)_en.vtt 8.2 KB
11 - Introduction to gym module/004 Getting started with Gym_en.vtt 8.2 KB
07 - Introduction to Artificial Intelligence/009 Value of the State_en.vtt 8.2 KB
05 - Introduction to MinMax Algorithm/003 Lookahead Problem_en.vtt 8.2 KB
11 - Introduction to gym module/002 Example of Gym Environment_en.vtt 8.2 KB
13 - Implementing Monte Carlo Predictions/002 Creating BlackJack Environment_en.vtt 8.1 KB
04 - Pygame Refresher/008 Movement of the shapes_en.vtt 8.1 KB
20 - Convolution Neural Network/006 BackPropagation_en.vtt 8.1 KB
10 - Implementation of Q-Learning to Find Optimal Path/004 Briefing about Q-Table_en.vtt 8.1 KB
06 - Creating TicTacToe using MinMax algorithm/001 introduction to Game_en.vtt 7.8 KB
10 - Implementation of Q-Learning to Find Optimal Path/007 Possible Actions_en.vtt 7.6 KB
09 - Bellman Equation and Dynamic Programming/003 Value Function_en.vtt 7.6 KB
04 - Pygame Refresher/007 Render a rectangle in the Screen_en.vtt 7.4 KB
12 - Monte Carlo Simulation/004 First Visit vs Every Visit MC_en.vtt 7.4 KB
06 - Creating TicTacToe using MinMax algorithm/007 Setting up Autoplayer (Artificial Intelligent Player)_en.vtt 7.3 KB
04 - Pygame Refresher/003 Introduction to Pygame shape_en.vtt 7.2 KB
04 - Pygame Refresher/005 Color Picker_en.vtt 7.0 KB
14 - Creating BlackJack Game/006 Introduction to Discount Parameter_en.vtt 6.8 KB
03 - Python Essentials/011 Learn to create conditions_en.vtt 6.8 KB
02 - Setup Anaconda and Install Dependencies for Project/001 Install Anaconda_en.vtt 6.7 KB
18 - TicTacToe Tensorflow/001 Introduction to Project Files_en.vtt 6.7 KB
04 - Pygame Refresher/009 Smoothen the movement using FPS_en.vtt 6.3 KB
03 - Python Essentials/021 Finite Game Loop_en.vtt 6.3 KB
04 - Pygame Refresher/001 Introduction to the pygame_en.vtt 6.3 KB
18 - TicTacToe Tensorflow/004 Define Independent (input) and Dependent (output) Variable_en.vtt 6.2 KB
21 - Deep Convolution Q-Learning Practical Pacman game/011 Fit the model_en.vtt 6.1 KB
12 - Monte Carlo Simulation/002 Monte Carlo Simulation_en.vtt 6.0 KB
02 - Setup Anaconda and Install Dependencies for Project/002 Create Virtual Environment_en.vtt 5.6 KB
17 - Tensorflow and Keras/007 Implementing Neural Network using Keras_en.vtt 5.5 KB
18 - TicTacToe Tensorflow/007 TicTacToe Model_en.vtt 5.3 KB
10 - Implementation of Q-Learning to Find Optimal Path/008 Iterations_en.vtt 5.3 KB
21 - Deep Convolution Q-Learning Practical Pacman game/007 Store Transition in Replay buffer_en.vtt 5.2 KB
17 - Tensorflow and Keras/005 Introduction to Keras_en.vtt 5.2 KB
20 - Convolution Neural Network/001 Introduction to convolution neural network_en.vtt 5.2 KB
20 - Convolution Neural Network/002 How ConvNet works_en.vtt 5.0 KB
07 - Introduction to Artificial Intelligence/010 Model_en.vtt 4.9 KB
03 - Python Essentials/005 Introduction to Strings in Python_en.vtt 4.9 KB
21 - Deep Convolution Q-Learning Practical Pacman game/015 Testing the game_en.vtt 4.8 KB
03 - Python Essentials/028 What is Object Oriented Programming_en.vtt 4.8 KB
09 - Bellman Equation and Dynamic Programming/001 Introduction_en.vtt 4.7 KB
09 - Bellman Equation and Dynamic Programming/002 Tribute to Bellman_en.vtt 4.0 KB
07 - Introduction to Artificial Intelligence/006 Typical RL scenario_en.vtt 4.0 KB
12 - Monte Carlo Simulation/005 BlackJack Example_en.vtt 3.9 KB
14 - Creating BlackJack Game/003 Q-Table_en.vtt 3.7 KB
15 - Neural Network Refresher/010 Why we use regularization in the Neural Network_en.vtt 3.6 KB
08 - Key Terms of Artificial Intelligence (Important)/002 Markov Reward Process_en.vtt 3.6 KB
20 - Convolution Neural Network/004 RELU Layer_en.vtt 3.5 KB
07 - Introduction to Artificial Intelligence/007 Policy_en.vtt 3.5 KB
15 - Neural Network Refresher/013 Introduction to mini-batch SGD_en.vtt 3.4 KB
20 - Convolution Neural Network/005 Pooling Layer_en.vtt 3.3 KB
11 - Introduction to gym module/001 The gym module_en.vtt 3.1 KB
03 - Python Essentials/027 Introduction to the section_en.vtt 3.1 KB
07 - Introduction to Artificial Intelligence/001 Motivation for Artificial Intelligence_en.vtt 3.0 KB
09 - Bellman Equation and Dynamic Programming/011 Q value for Non-Deterministic Environment_en.vtt 2.9 KB
03 - Python Essentials/012 is operator in Python_en.vtt 2.9 KB
03 - Python Essentials/016 Introduction to Data Structures_en.vtt 2.7 KB
07 - Introduction to Artificial Intelligence/004 Rewards_en.vtt 2.7 KB
07 - Introduction to Artificial Intelligence/002 Reinforcement Learning_en.vtt 2.7 KB
07 - Introduction to Artificial Intelligence/003 Environment_en.vtt 2.6 KB
05 - Introduction to MinMax Algorithm/010 MinMax Algorithm_en.vtt 2.1 KB
03 - Python Essentials/001 What is Python_en.vtt 2.0 KB
01 - Introduction/001 Introduction_en.vtt 1.6 KB
18 - TicTacToe Tensorflow/006 Predict from the model_en.vtt 1.5 KB
03 - Python Essentials/002 Introduction to the data types_en.vtt 1.4 KB
03 - Python Essentials/010 Introduction to Booleans in Python_en.vtt 1.3 KB
07 - Introduction to Artificial Intelligence/005 Path_en.vtt 1.2 KB
03 - Python Essentials/008 Introduction to the variables_en.vtt 992 bytes
03 - Python Essentials/014 Introduction to conditional statements_en.vtt 935 bytes
07 - Introduction to Artificial Intelligence/008 Rewards_en.vtt 912 bytes
03 - Python Essentials/024 What is Function and Why we need it_en.vtt 870 bytes
03 - Python Essentials/019 Introduction to the loops in Python_en.vtt 679 bytes
22 - Any games you want to suggest/001 Farewell.html 339 bytes

Learn To Create Artificially Intelligent Games Using Python3

File List

Download Torrent

Related Resources

Copyright Infringement