[Udemy] Practical AI with Python and Reinforcement Learning (07.2021)

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文档列表

11 Classical Q Learning/019 Q-Learning Exercise Project - Solutions.mp4 185.7 MB
10 Open AI Gym Overview/003 OpenAI Gym - Documentation Tour.mp4 153.7 MB
07 Artificial Neural Network and TensorFlow Basics/027 Tensorboard.mp4 151.2 MB
07 Artificial Neural Network and TensorFlow Basics/020 Keras Project Solution - Exploratoy Data Analysis.mp4 150.6 MB
10 Open AI Gym Overview/005 OpenAI Gym - Working with the Environment.mp4 144.2 MB
07 Artificial Neural Network and TensorFlow Basics/012 Keras Regression - Exploratory Data Analysis.mp4 143.6 MB
07 Artificial Neural Network and TensorFlow Basics/023 Keras Project Solutions - Categorical Data.mp4 131.1 MB
04 Matplotlib and Visualization Overview/011 Matplotlib Exercise Questions - Solutions.mp4 129.1 MB
10 Open AI Gym Overview/006 OpenAI Gym - Agent Interacting with the Environment.mp4 122.1 MB
06 Pandas and Scikit-Learn Crash Course/004 Pandas - DataFrames - Part One.mp4 120.0 MB
07 Artificial Neural Network and TensorFlow Basics/017 Keras Classification - Overfitting and Evaluation.mp4 116.6 MB
03 Numpy Basics Overview/002 NumPy Arrays.mp4 115.0 MB
11 Classical Q Learning/017 Continuous Q-Learning - Part Six - Training and Usage.mp4 114.1 MB
11 Classical Q Learning/010 Q-Learning Implementation - Part Four - Agent Training.mp4 112.2 MB
12 Deep Q-Learning/011 DQN Manual Implementation - Part Four - Model Training.mp4 112.1 MB
08 Convolutional Neural Networks with TensorFlow/007 CNN on MNIST - Creating and Training the Model.mp4 103.7 MB
02 Course Set-Up and Installation Procedures/001 Anaconda and Jupyter Notebook Install and Setup.mp4 103.6 MB
07 Artificial Neural Network and TensorFlow Basics/021 Keras Project Solutions - Missing Data - Part One.mp4 101.5 MB
06 Pandas and Scikit-Learn Crash Course/007 Pandas - DataFrames - Part Four.mp4 101.4 MB
04 Matplotlib and Visualization Overview/006 Matplotlib - Subplots Functionality.mp4 100.9 MB
12 Deep Q-Learning/006 DQN Theory and Intuition - Part Four - Experience Replay.mp4 100.7 MB
11 Classical Q Learning/009 Q-Learning Implementation - Part Three - Update Functions.mp4 97.4 MB
08 Convolutional Neural Networks with TensorFlow/014 CNN on Real Image Files - Creating the Model.mp4 94.9 MB
06 Pandas and Scikit-Learn Crash Course/006 Pandas - DataFrames - Part Three.mp4 93.9 MB
12 Deep Q-Learning/005 DQN Theory and Intuition - Part Three - Feedback and Function Approximation.mp4 92.6 MB
11 Classical Q Learning/007 Q-Learning Implementation - Part One - Environment Setup.mp4 92.5 MB
08 Convolutional Neural Networks with TensorFlow/013 CNN on Real Image Files - Data Generation.mp4 92.0 MB
11 Classical Q Learning/015 Continuous Q-Learning - Part Four - Discretization Implementation.mp4 90.4 MB
11 Classical Q Learning/013 Continuous Q-Learning Theory - Part Two- Q-Table Shape.mp4 89.9 MB
07 Artificial Neural Network and TensorFlow Basics/022 Keras Project Solutions - Dealing with Missing Data - Part Two.mp4 89.6 MB
12 Deep Q-Learning/010 DQN Manual Implementation - Part Three - Hyperparameters and Functions.mp4 88.9 MB
07 Artificial Neural Network and TensorFlow Basics/010 Keras Syntax - Creating and Training the Model.mp4 88.4 MB
12 Deep Q-Learning/015 DQN - Keras-RL2 - Part Four - DQN Agent.mp4 88.2 MB
12 Deep Q-Learning/007 DQN Theory and Intuition - Part Five - Mapping Key Ideas to Code.mp4 85.5 MB
04 Matplotlib and Visualization Overview/008 Matplotlib Styling - Colors and Styles.mp4 85.1 MB
07 Artificial Neural Network and TensorFlow Basics/019 Keras Project Notebook Exercise Overview.mp4 84.5 MB
08 Convolutional Neural Networks with TensorFlow/012 CNN on Real Image Files - Reading in the Data.mp4 84.3 MB
11 Classical Q Learning/003 Q-Learning Theory - Part One - Table Intuition.mp4 81.3 MB
06 Pandas and Scikit-Learn Crash Course/009 Scikit-Learn - Using Metrics.mp4 81.0 MB
07 Artificial Neural Network and TensorFlow Basics/013 Keras Regression - EDA Continued.mp4 80.0 MB
07 Artificial Neural Network and TensorFlow Basics/006 Cost Functions and Gradient Descent.mp4 79.7 MB
08 Convolutional Neural Networks with TensorFlow/002 Image Filters and Kernels.mp4 75.8 MB
05 Machine Learning, Deep Learning, and Reinforcement Learning/002 Supervised Machine Learning Process.mp4 75.2 MB
02 Course Set-Up and Installation Procedures/003 Environment Setup Walkthrough.mp4 74.7 MB
10 Open AI Gym Overview/002 OpenAI Overview and History.mp4 73.1 MB
07 Artificial Neural Network and TensorFlow Basics/015 Keras Regression - Model Evaluation and Predictions.mp4 72.3 MB
11 Classical Q Learning/018 Q-Learning Exercise Project.mp4 69.5 MB
07 Artificial Neural Network and TensorFlow Basics/011 Keras Syntax - Model Evaluation.mp4 67.9 MB
08 Convolutional Neural Networks with TensorFlow/009 CNN on CIFAR-10 - The Data.mp4 67.4 MB
07 Artificial Neural Network and TensorFlow Basics/026 Keras Project Solutions - Model Evaluation.mp4 66.2 MB
12 Deep Q-Learning/017 DQN - Exercise Solutions.mp4 65.6 MB
07 Artificial Neural Network and TensorFlow Basics/004 Activation Functions.mp4 65.6 MB
09 Reinforcement Learning - Core Concepts/002 Agents, Environments, and Policy.mp4 65.3 MB
06 Pandas and Scikit-Learn Crash Course/008 Scikit-Learn - Using Train-Test-Split.mp4 63.4 MB
08 Convolutional Neural Networks with TensorFlow/006 CNN on MNIST - The Data.mp4 62.7 MB
04 Matplotlib and Visualization Overview/004 Matplotlib - Implementing Figures and Axes.mp4 61.9 MB
11 Classical Q Learning/006 Q-Learning Theory - Part Four - Programmatic Q Updates.mp4 61.0 MB
08 Convolutional Neural Networks with TensorFlow/003 Convolutional Layers.mp4 60.8 MB
07 Artificial Neural Network and TensorFlow Basics/007 Backpropagation.mp4 60.8 MB
09 Reinforcement Learning - Core Concepts/003 Rewards, Discount Factors, and Bellman Equation.mp4 59.5 MB
11 Classical Q Learning/011 Q-Learning Implementation - Part Five - Visualization and Utilization.mp4 59.3 MB
07 Artificial Neural Network and TensorFlow Basics/016 Keras Classification - EDA and Preprocessing.mp4 58.9 MB
08 Convolutional Neural Networks with TensorFlow/017 CNN Exercise Project Solutions.mp4 58.6 MB
01 Course Overview/002 COURSE_NOTEBOOKS.zip 58.0 MB
02 Course Set-Up and Installation Procedures/004 COURSE_NOTEBOOKS.zip 58.0 MB
05 Machine Learning, Deep Learning, and Reinforcement Learning/001 What is Machine Learning, Deep Learning, and Artificial Intelligence_.mp4 57.3 MB
11 Classical Q Learning/012 Continuous Q-Learning Theory - Part One - Environment Setup.mp4 57.1 MB
11 Classical Q Learning/004 Q-Learning Theory - Part Two - Q Target Equation.mp4 56.9 MB
06 Pandas and Scikit-Learn Crash Course/005 Pandas - DataFrames - Part Two.mp4 56.7 MB
12 Deep Q-Learning/012 DQN - Keras-RL2 - Part One - Overview.mp4 56.3 MB
04 Matplotlib and Visualization Overview/002 Matplotlib Basics.mp4 56.2 MB
04 Matplotlib and Visualization Overview/010 Matplotlib Exercise Questions Overview.mp4 53.2 MB
07 Artificial Neural Network and TensorFlow Basics/009 Keras Syntax - Preparing the Data.mp4 52.8 MB
03 Numpy Basics Overview/004 Numpy Operations - Part Two.mp4 51.0 MB
03 Numpy Basics Overview/006 Numpy Exercise Solutions.mp4 50.9 MB
07 Artificial Neural Network and TensorFlow Basics/002 Perceptron Model.mp4 50.3 MB
07 Artificial Neural Network and TensorFlow Basics/014 Keras Regression - Data Preprocessing and Model Creation.mp4 49.3 MB
08 Convolutional Neural Networks with TensorFlow/015 CNN on Real Image Files - Model Evaluation.mp4 49.2 MB
12 Deep Q-Learning/004 DQN Theory and Intuition - Part Two - Neural Networks for RL.mp4 48.8 MB
03 Numpy Basics Overview/003 Numpy Operations - Part One.mp4 48.6 MB
07 Artificial Neural Network and TensorFlow Basics/005 Multi-Class Classification Considerations.mp4 48.3 MB
06 Pandas and Scikit-Learn Crash Course/003 Pandas - Series Part Two.mp4 47.7 MB
11 Classical Q Learning/016 Continuous Q-Learning - Part Five - Functions and Hyperparameters.mp4 47.6 MB
08 Convolutional Neural Networks with TensorFlow/010 CNN on CIFAR-10 - Evaluating the Model.mp4 47.6 MB
01 Course Overview/002 Course Curriculum Overview.mp4 46.1 MB
11 Classical Q Learning/008 Q-Learning Implementation - Part Two - Table and Hyperparameters.mp4 45.2 MB
01 Course Overview/003 Course Success and Overview.mp4 44.1 MB
04 Matplotlib and Visualization Overview/009 Advanced Matplotlib Commands (Optional).mp4 42.4 MB
06 Pandas and Scikit-Learn Crash Course/002 Pandas - Series Part One.mp4 40.5 MB
08 Convolutional Neural Networks with TensorFlow/008 CNN on MNIST - Model Evaluation.mp4 40.3 MB
11 Classical Q Learning/005 Q-Learning Theory - Part Three - Q-Update Equation.mp4 39.3 MB
10 Open AI Gym Overview/004 OpenAI Gym - Environment Key Ideas.mp4 39.1 MB
07 Artificial Neural Network and TensorFlow Basics/003 Neural Networks.mp4 37.7 MB
04 Matplotlib and Visualization Overview/007 Matplotlib Styling - Legends.mp4 35.7 MB
12 Deep Q-Learning/009 DQN Manual Implementation - Part Two - Artificial Neural Network.mp4 33.5 MB
07 Artificial Neural Network and TensorFlow Basics/025 Keras Project Solutions- Creating and Training the Model.mp4 31.2 MB
12 Deep Q-Learning/002 History of DQN.mp4 30.1 MB
12 Deep Q-Learning/016 DQN - Exercise Overview.mp4 29.7 MB
08 Convolutional Neural Networks with TensorFlow/011 Downloading Data Set for Real Image Lectures.mp4 29.5 MB
08 Convolutional Neural Networks with TensorFlow/004 Pooling Layers.mp4 29.0 MB
09 Reinforcement Learning - Core Concepts/004 Deterministic vs. Stochastic Processes.mp4 28.6 MB
11 Classical Q Learning/002 History of Q-Learning.mp4 28.4 MB
12 Deep Q-Learning/014 DQN - Keras-RL2 - Part Three - Creating the ANN.mp4 27.3 MB
04 Matplotlib and Visualization Overview/003 Matplotlib - Understanding the Figure Object.mp4 27.1 MB
08 Convolutional Neural Networks with TensorFlow/005 MNIST Data Set Overview.mp4 25.9 MB
11 Classical Q Learning/014 Continuous Q-Learning Theory - Part Three - Discretization Theory.mp4 25.9 MB
07 Artificial Neural Network and TensorFlow Basics/024 Keras Project Solutions - Data Preprocessing.mp4 25.1 MB
12 Deep Q-Learning/003 DQN Theory and Intuition - Part One - Review of Core RL Ideas.mp4 25.1 MB
04 Matplotlib and Visualization Overview/005 Matplotlib - Figure Parameters.mp4 24.9 MB
11 Classical Q Learning/001 Introduction to Classical Q-Learning Overview.mp4 23.7 MB
04 Matplotlib and Visualization Overview/001 Introduction to Matplotlib.mp4 22.6 MB
12 Deep Q-Learning/008 DQN Manual Implementation - Part One - Imports and Environment.mp4 21.8 MB
08 Convolutional Neural Networks with TensorFlow/016 CNN Exercise Project Overview.mp4 18.7 MB
12 Deep Q-Learning/013 DQN - Keras-RL2 - Part Two - Imports and Environment.mp4 14.6 MB
03 Numpy Basics Overview/005 Numpy Exercise Overview.mp4 12.1 MB
03 Numpy Basics Overview/001 Introduction to Numpy Section.mp4 11.8 MB
09 Reinforcement Learning - Core Concepts/001 Overview of Core Concepts for Reinforcement Learning Section.mp4 11.2 MB
07 Artificial Neural Network and TensorFlow Basics/008 TensorFlow vs. Keras Explained.mp4 10.9 MB
12 Deep Q-Learning/001 DQN Section Overview.mp4 10.6 MB
07 Artificial Neural Network and TensorFlow Basics/001 Introduction to Artificial Neural Networks.mp4 10.1 MB
07 Artificial Neural Network and TensorFlow Basics/018 Keras Classification - Overview of Project Options.mp4 8.2 MB
08 Convolutional Neural Networks with TensorFlow/001 Convolutional Neural Networks Section Overview.mp4 7.9 MB
10 Open AI Gym Overview/001 Introduction to OpenAI Gym Section.mp4 6.4 MB
12 Deep Q-Learning/110 DQNNaturePaper.pdf 4.6 MB
10 Open AI Gym Overview/005 OpenAI Gym - Working with the Environment.en.srt 44.5 kB
11 Classical Q Learning/019 Q-Learning Exercise Project - Solutions.en.srt 34.3 kB
03 Numpy Basics Overview/002 NumPy Arrays.en.srt 33.9 kB
10 Open AI Gym Overview/006 OpenAI Gym - Agent Interacting with the Environment.en.srt 32.8 kB
11 Classical Q Learning/017 Continuous Q-Learning - Part Six - Training and Usage.en.srt 32.5 kB
12 Deep Q-Learning/005 DQN Theory and Intuition - Part Three - Feedback and Function Approximation.en.srt 32.2 kB
07 Artificial Neural Network and TensorFlow Basics/027 Tensorboard.en.srt 31.4 kB
06 Pandas and Scikit-Learn Crash Course/004 Pandas - DataFrames - Part One.en.srt 30.8 kB
07 Artificial Neural Network and TensorFlow Basics/020 Keras Project Solution - Exploratoy Data Analysis.en.srt 30.8 kB
12 Deep Q-Learning/006 DQN Theory and Intuition - Part Four - Experience Replay.en.srt 30.5 kB
04 Matplotlib and Visualization Overview/006 Matplotlib - Subplots Functionality.en.srt 30.4 kB
07 Artificial Neural Network and TensorFlow Basics/006 Cost Functions and Gradient Descent.en.srt 29.4 kB
07 Artificial Neural Network and TensorFlow Basics/012 Keras Regression - Exploratory Data Analysis.en.srt 28.7 kB
12 Deep Q-Learning/010 DQN Manual Implementation - Part Three - Hyperparameters and Functions.en.srt 28.5 kB
07 Artificial Neural Network and TensorFlow Basics/023 Keras Project Solutions - Categorical Data.en.srt 27.8 kB
11 Classical Q Learning/015 Continuous Q-Learning - Part Four - Discretization Implementation.en.srt 27.7 kB
11 Classical Q Learning/010 Q-Learning Implementation - Part Four - Agent Training.en.srt 27.4 kB
08 Convolutional Neural Networks with TensorFlow/007 CNN on MNIST - Creating and Training the Model.en.srt 26.6 kB
12 Deep Q-Learning/011 DQN Manual Implementation - Part Four - Model Training.en.srt 26.3 kB
04 Matplotlib and Visualization Overview/011 Matplotlib Exercise Questions - Solutions.en.srt 26.2 kB
11 Classical Q Learning/009 Q-Learning Implementation - Part Three - Update Functions.en.srt 26.2 kB
11 Classical Q Learning/013 Continuous Q-Learning Theory - Part Two- Q-Table Shape.en.srt 26.2 kB
07 Artificial Neural Network and TensorFlow Basics/017 Keras Classification - Overfitting and Evaluation.en.srt 25.9 kB
08 Convolutional Neural Networks with TensorFlow/013 CNN on Real Image Files - Data Generation.en.srt 24.9 kB
11 Classical Q Learning/007 Q-Learning Implementation - Part One - Environment Setup.en.srt 24.7 kB
12 Deep Q-Learning/007 DQN Theory and Intuition - Part Five - Mapping Key Ideas to Code.en.srt 24.6 kB
11 Classical Q Learning/003 Q-Learning Theory - Part One - Table Intuition.en.srt 24.0 kB
10 Open AI Gym Overview/003 OpenAI Gym - Documentation Tour.en.srt 23.7 kB
12 Deep Q-Learning/015 DQN - Keras-RL2 - Part Four - DQN Agent.en.srt 23.7 kB
06 Pandas and Scikit-Learn Crash Course/009 Scikit-Learn - Using Metrics.en.srt 23.0 kB
02 Course Set-Up and Installation Procedures/001 Anaconda and Jupyter Notebook Install and Setup.en.srt 22.9 kB
08 Convolutional Neural Networks with TensorFlow/003 Convolutional Layers.en.srt 22.6 kB
06 Pandas and Scikit-Learn Crash Course/007 Pandas - DataFrames - Part Four.en.srt 22.4 kB
04 Matplotlib and Visualization Overview/008 Matplotlib Styling - Colors and Styles.en.srt 22.4 kB
07 Artificial Neural Network and TensorFlow Basics/007 Backpropagation.en.srt 22.4 kB
07 Artificial Neural Network and TensorFlow Basics/021 Keras Project Solutions - Missing Data - Part One.en.srt 22.3 kB
04 Matplotlib and Visualization Overview/004 Matplotlib - Implementing Figures and Axes.en.srt 22.3 kB
08 Convolutional Neural Networks with TensorFlow/012 CNN on Real Image Files - Reading in the Data.en.srt 22.2 kB
07 Artificial Neural Network and TensorFlow Basics/010 Keras Syntax - Creating and Training the Model.en.srt 22.0 kB
06 Pandas and Scikit-Learn Crash Course/006 Pandas - DataFrames - Part Three.en.srt 21.9 kB
08 Convolutional Neural Networks with TensorFlow/014 CNN on Real Image Files - Creating the Model.en.srt 21.7 kB
11 Classical Q Learning/012 Continuous Q-Learning Theory - Part One - Environment Setup.en.srt 21.5 kB
05 Machine Learning, Deep Learning, and Reinforcement Learning/002 Supervised Machine Learning Process.en.srt 20.9 kB
04 Matplotlib and Visualization Overview/002 Matplotlib Basics.en.srt 20.9 kB
09 Reinforcement Learning - Core Concepts/003 Rewards, Discount Factors, and Bellman Equation.en.srt 20.8 kB
07 Artificial Neural Network and TensorFlow Basics/013 Keras Regression - EDA Continued.en.srt 20.4 kB
08 Convolutional Neural Networks with TensorFlow/006 CNN on MNIST - The Data.en.srt 19.9 kB
09 Reinforcement Learning - Core Concepts/002 Agents, Environments, and Policy.en.srt 19.4 kB
07 Artificial Neural Network and TensorFlow Basics/022 Keras Project Solutions - Dealing with Missing Data - Part Two.en.srt 19.3 kB
08 Convolutional Neural Networks with TensorFlow/002 Image Filters and Kernels.en.srt 19.1 kB
07 Artificial Neural Network and TensorFlow Basics/011 Keras Syntax - Model Evaluation.en.srt 19.0 kB
08 Convolutional Neural Networks with TensorFlow/009 CNN on CIFAR-10 - The Data.en.srt 18.6 kB
06 Pandas and Scikit-Learn Crash Course/008 Scikit-Learn - Using Train-Test-Split.en.srt 18.5 kB
10 Open AI Gym Overview/002 OpenAI Overview and History.en.srt 18.2 kB
07 Artificial Neural Network and TensorFlow Basics/004 Activation Functions.en.srt 17.8 kB
02 Course Set-Up and Installation Procedures/003 Environment Setup Walkthrough.en.srt 17.8 kB
11 Classical Q Learning/008 Q-Learning Implementation - Part Two - Table and Hyperparameters.en.srt 17.7 kB
12 Deep Q-Learning/004 DQN Theory and Intuition - Part Two - Neural Networks for RL.en.srt 17.5 kB
05 Machine Learning, Deep Learning, and Reinforcement Learning/001 What is Machine Learning, Deep Learning, and Artificial Intelligence_.en.srt 17.4 kB
07 Artificial Neural Network and TensorFlow Basics/015 Keras Regression - Model Evaluation and Predictions.en.srt 17.3 kB
03 Numpy Basics Overview/003 Numpy Operations - Part One.en.srt 17.3 kB
07 Artificial Neural Network and TensorFlow Basics/005 Multi-Class Classification Considerations.en.srt 17.2 kB
11 Classical Q Learning/004 Q-Learning Theory - Part Two - Q Target Equation.en.srt 17.2 kB
11 Classical Q Learning/016 Continuous Q-Learning - Part Five - Functions and Hyperparameters.en.srt 16.7 kB
07 Artificial Neural Network and TensorFlow Basics/009 Keras Syntax - Preparing the Data.en.srt 16.6 kB
11 Classical Q Learning/011 Q-Learning Implementation - Part Five - Visualization and Utilization.en.srt 16.4 kB
07 Artificial Neural Network and TensorFlow Basics/002 Perceptron Model.en.srt 16.4 kB
06 Pandas and Scikit-Learn Crash Course/003 Pandas - Series Part Two.en.srt 16.3 kB
01 Course Overview/002 Course Curriculum Overview.en.srt 16.2 kB
12 Deep Q-Learning/017 DQN - Exercise Solutions.en.srt 15.8 kB
11 Classical Q Learning/006 Q-Learning Theory - Part Four - Programmatic Q Updates.en.srt 15.7 kB
07 Artificial Neural Network and TensorFlow Basics/026 Keras Project Solutions - Model Evaluation.en.srt 15.1 kB
06 Pandas and Scikit-Learn Crash Course/002 Pandas - Series Part One.en.srt 14.2 kB
06 Pandas and Scikit-Learn Crash Course/005 Pandas - DataFrames - Part Two.en.srt 14.1 kB
10 Open AI Gym Overview/004 OpenAI Gym - Environment Key Ideas.en.srt 13.9 kB
07 Artificial Neural Network and TensorFlow Basics/019 Keras Project Notebook Exercise Overview.en.srt 13.4 kB
08 Convolutional Neural Networks with TensorFlow/015 CNN on Real Image Files - Model Evaluation.en.srt 13.2 kB
08 Convolutional Neural Networks with TensorFlow/017 CNN Exercise Project Solutions.en.srt 13.1 kB
07 Artificial Neural Network and TensorFlow Basics/014 Keras Regression - Data Preprocessing and Model Creation.en.srt 13.0 kB
03 Numpy Basics Overview/004 Numpy Operations - Part Two.en.srt 12.8 kB
11 Classical Q Learning/018 Q-Learning Exercise Project.en.srt 12.7 kB
07 Artificial Neural Network and TensorFlow Basics/016 Keras Classification - EDA and Preprocessing.en.srt 12.5 kB
01 Course Overview/003 Course Success and Overview.en.srt 12.3 kB
04 Matplotlib and Visualization Overview/003 Matplotlib - Understanding the Figure Object.en.srt 12.2 kB
12 Deep Q-Learning/012 DQN - Keras-RL2 - Part One - Overview.en.srt 12.0 kB
11 Classical Q Learning/005 Q-Learning Theory - Part Three - Q-Update Equation.en.srt 11.9 kB
07 Artificial Neural Network and TensorFlow Basics/003 Neural Networks.en.srt 11.9 kB
12 Deep Q-Learning/009 DQN Manual Implementation - Part Two - Artificial Neural Network.en.srt 11.8 kB
08 Convolutional Neural Networks with TensorFlow/010 CNN on CIFAR-10 - Evaluating the Model.en.srt 11.6 kB
03 Numpy Basics Overview/006 Numpy Exercise Solutions.en.srt 11.6 kB
08 Convolutional Neural Networks with TensorFlow/004 Pooling Layers.en.srt 11.3 kB
04 Matplotlib and Visualization Overview/007 Matplotlib Styling - Legends.en.srt 11.0 kB
08 Convolutional Neural Networks with TensorFlow/008 CNN on MNIST - Model Evaluation.en.srt 10.3 kB
04 Matplotlib and Visualization Overview/010 Matplotlib Exercise Questions Overview.en.srt 9.9 kB
08 Convolutional Neural Networks with TensorFlow/011 Downloading Data Set for Real Image Lectures.en.srt 9.3 kB
12 Deep Q-Learning/014 DQN - Keras-RL2 - Part Three - Creating the ANN.en.srt 9.2 kB
09 Reinforcement Learning - Core Concepts/004 Deterministic vs. Stochastic Processes.en.srt 8.2 kB
04 Matplotlib and Visualization Overview/005 Matplotlib - Figure Parameters.en.srt 8.1 kB
12 Deep Q-Learning/008 DQN Manual Implementation - Part One - Imports and Environment.en.srt 8.0 kB
11 Classical Q Learning/014 Continuous Q-Learning Theory - Part Three - Discretization Theory.en.srt 8.0 kB
08 Convolutional Neural Networks with TensorFlow/005 MNIST Data Set Overview.en.srt 7.8 kB
12 Deep Q-Learning/003 DQN Theory and Intuition - Part One - Review of Core RL Ideas.en.srt 7.5 kB
04 Matplotlib and Visualization Overview/001 Introduction to Matplotlib.en.srt 7.1 kB
12 Deep Q-Learning/002 History of DQN.en.srt 7.1 kB
04 Matplotlib and Visualization Overview/009 Advanced Matplotlib Commands (Optional).en.srt 6.9 kB
11 Classical Q Learning/001 Introduction to Classical Q-Learning Overview.en.srt 6.6 kB
07 Artificial Neural Network and TensorFlow Basics/025 Keras Project Solutions- Creating and Training the Model.en.srt 6.2 kB
12 Deep Q-Learning/016 DQN - Exercise Overview.en.srt 6.0 kB
11 Classical Q Learning/002 History of Q-Learning.en.srt 6.0 kB
07 Artificial Neural Network and TensorFlow Basics/024 Keras Project Solutions - Data Preprocessing.en.srt 5.6 kB
12 Deep Q-Learning/013 DQN - Keras-RL2 - Part Two - Imports and Environment.en.srt 5.1 kB
06 Pandas and Scikit-Learn Crash Course/033 Advertising.csv 4.2 kB
08 Convolutional Neural Networks with TensorFlow/016 CNN Exercise Project Overview.en.srt 4.1 kB
07 Artificial Neural Network and TensorFlow Basics/001 Introduction to Artificial Neural Networks.en.srt 3.5 kB
07 Artificial Neural Network and TensorFlow Basics/008 TensorFlow vs. Keras Explained.en.srt 3.2 kB
03 Numpy Basics Overview/001 Introduction to Numpy Section.en.srt 3.2 kB
12 Deep Q-Learning/001 DQN Section Overview.en.srt 3.2 kB
09 Reinforcement Learning - Core Concepts/001 Overview of Core Concepts for Reinforcement Learning Section.en.srt 2.8 kB
01 Course Overview/001 Welcome Message.html 2.8 kB
08 Convolutional Neural Networks with TensorFlow/001 Convolutional Neural Networks Section Overview.en.srt 2.8 kB
07 Artificial Neural Network and TensorFlow Basics/018 Keras Classification - Overview of Project Options.en.srt 2.7 kB
03 Numpy Basics Overview/005 Numpy Exercise Overview.en.srt 2.2 kB
10 Open AI Gym Overview/001 Introduction to OpenAI Gym Section.en.srt 1.7 kB
02 Course Set-Up and Installation Procedures/002 Note on Environment Setup.html 1.6 kB
06 Pandas and Scikit-Learn Crash Course/001 Pandas and Scikit-Learn Overview.html 1.1 kB
09 Reinforcement Learning - Core Concepts/005 Tabular Reinforcement Learning.html 1.1 kB
08 Convolutional Neural Networks with TensorFlow/external-assets-links.txt 180 Bytes

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