Learn To Create Artificially Intelligent Games Using Python3

Creation Time	May 30, 2024, 5:34 p.m.
Last Access Time	Feb. 10, 2026, 10:08 a.m.
File Size	12.6 GB
Keywords	Create To Games Artificially Learn Using Intelligent Python3
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Total Files	387
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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

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