Learn To Create Artificially Intelligent Games Using Python3

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Learn To Create Artificially Intelligent Games Using Python3

磁力链接/BT种子简介

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收录时间：2023-12-20
最近下载：2025-12-14

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文件列表

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