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| 001 Anaconda and Jupyter Notebook Install and Setup.en.srt |
22.34Кб |
| 001 Anaconda and Jupyter Notebook Install and Setup.mp4 |
98.78Мб |
| 001 Convolutional Neural Networks Section Overview.en.srt |
2.69Кб |
| 001 Convolutional Neural Networks Section Overview.mp4 |
7.51Мб |
| 001 DQN Section Overview.en.srt |
3.10Кб |
| 001 DQN Section Overview.mp4 |
10.11Мб |
| 001 Introduction to Artificial Neural Networks.en.srt |
3.41Кб |
| 001 Introduction to Artificial Neural Networks.mp4 |
9.66Мб |
| 001 Introduction to Classical Q-Learning Overview.en.srt |
6.43Кб |
| 001 Introduction to Classical Q-Learning Overview.mp4 |
22.61Мб |
| 001 Introduction to Matplotlib.en.srt |
6.94Кб |
| 001 Introduction to Matplotlib.mp4 |
21.58Мб |
| 001 Introduction to Numpy Section.en.srt |
3.12Кб |
| 001 Introduction to Numpy Section.mp4 |
11.29Мб |
| 001 Introduction to OpenAI Gym Section.en.srt |
1.62Кб |
| 001 Introduction to OpenAI Gym Section.mp4 |
6.07Мб |
| 001 Overview of Core Concepts for Reinforcement Learning Section.en.srt |
2.77Кб |
| 001 Overview of Core Concepts for Reinforcement Learning Section.mp4 |
10.69Мб |
| 001 Pandas and Scikit-Learn Overview.html |
1.11Кб |
| 001 Welcome Message.html |
2.72Кб |
| 001 What is Machine Learning, Deep Learning, and Artificial Intelligence_.en.srt |
17.00Кб |
| 001 What is Machine Learning, Deep Learning, and Artificial Intelligence_.mp4 |
54.62Мб |
| 002 Agents, Environments, and Policy.en.srt |
18.91Кб |
| 002 Agents, Environments, and Policy.mp4 |
62.31Мб |
| 002 COURSE_NOTEBOOKS.zip |
55.33Мб |
| 002 Course Curriculum Overview.en.srt |
15.82Кб |
| 002 Course Curriculum Overview.mp4 |
43.95Мб |
| 002 History of DQN.en.srt |
6.92Кб |
| 002 History of DQN.mp4 |
28.75Мб |
| 002 History of Q-Learning.en.srt |
5.83Кб |
| 002 History of Q-Learning.mp4 |
27.07Мб |
| 002 Image Filters and Kernels.en.srt |
18.65Кб |
| 002 Image Filters and Kernels.mp4 |
72.33Мб |
| 002 Matplotlib Basics.en.srt |
20.39Кб |
| 002 Matplotlib Basics.mp4 |
53.61Мб |
| 002 Note on Environment Setup.html |
1.59Кб |
| 002 NumPy Arrays.en.srt |
33.09Кб |
| 002 NumPy Arrays.mp4 |
109.65Мб |
| 002 OpenAI Overview and History.en.srt |
17.81Кб |
| 002 OpenAI Overview and History.mp4 |
69.73Мб |
| 002 Pandas - Series Part One.en.srt |
13.88Кб |
| 002 Pandas - Series Part One.mp4 |
38.65Мб |
| 002 Perceptron Model.en.srt |
15.97Кб |
| 002 Perceptron Model.mp4 |
48.01Мб |
| 002 Supervised Machine Learning Process.en.srt |
20.44Кб |
| 002 Supervised Machine Learning Process.mp4 |
71.69Мб |
| 003 Convolutional Layers.en.srt |
22.05Кб |
| 003 Convolutional Layers.mp4 |
57.98Мб |
| 003 Course Success and Overview.en.srt |
11.97Кб |
| 003 Course Success and Overview.mp4 |
42.06Мб |
| 003 DQN Theory and Intuition - Part One - Review of Core RL Ideas.en.srt |
7.37Кб |
| 003 DQN Theory and Intuition - Part One - Review of Core RL Ideas.mp4 |
23.94Мб |
| 003 Environment Setup Walkthrough.en.srt |
17.36Кб |
| 003 Environment Setup Walkthrough.mp4 |
71.19Мб |
| 003 Matplotlib - Understanding the Figure Object.en.srt |
11.96Кб |
| 003 Matplotlib - Understanding the Figure Object.mp4 |
25.81Мб |
| 003 Neural Networks.en.srt |
11.58Кб |
| 003 Neural Networks.mp4 |
35.91Мб |
| 003 Numpy Operations - Part One.en.srt |
16.85Кб |
| 003 Numpy Operations - Part One.mp4 |
46.36Мб |
| 003 OpenAI Gym - Documentation Tour.en.srt |
23.19Кб |
| 003 OpenAI Gym - Documentation Tour.mp4 |
146.53Мб |
| 003 Pandas - Series Part Two.en.srt |
15.96Кб |
| 003 Pandas - Series Part Two.mp4 |
45.49Мб |
| 003 Q-Learning Theory - Part One - Table Intuition.en.srt |
23.41Кб |
| 003 Q-Learning Theory - Part One - Table Intuition.mp4 |
77.51Мб |
| 003 Rewards, Discount Factors, and Bellman Equation.en.srt |
20.31Кб |
| 003 Rewards, Discount Factors, and Bellman Equation.mp4 |
56.74Мб |
| 004 Activation Functions.en.srt |
17.42Кб |
| 004 Activation Functions.mp4 |
62.52Мб |
| 004 COURSE_NOTEBOOKS.zip |
55.33Мб |
| 004 Deterministic vs. Stochastic Processes.en.srt |
8.05Кб |
| 004 Deterministic vs. Stochastic Processes.mp4 |
27.29Мб |
| 004 DQN Theory and Intuition - Part Two - Neural Networks for RL.en.srt |
17.09Кб |
| 004 DQN Theory and Intuition - Part Two - Neural Networks for RL.mp4 |
46.52Мб |
| 004 Matplotlib - Implementing Figures and Axes.en.srt |
21.79Кб |
| 004 Matplotlib - Implementing Figures and Axes.mp4 |
59.05Мб |
| 004 Numpy Operations - Part Two.en.srt |
12.51Кб |
| 004 Numpy Operations - Part Two.mp4 |
48.64Мб |
| 004 OpenAI Gym - Environment Key Ideas.en.srt |
13.53Кб |
| 004 OpenAI Gym - Environment Key Ideas.mp4 |
37.28Мб |
| 004 Pandas - DataFrames - Part One.en.srt |
30.10Кб |
| 004 Pandas - DataFrames - Part One.mp4 |
114.40Мб |
| 004 Pooling Layers.en.srt |
10.99Кб |
| 004 Pooling Layers.mp4 |
27.64Мб |
| 004 Q-Learning Theory - Part Two - Q Target Equation.en.srt |
16.79Кб |
| 004 Q-Learning Theory - Part Two - Q Target Equation.mp4 |
54.29Мб |
| 005 DQN Theory and Intuition - Part Three - Feedback and Function Approximation.en.srt |
31.44Кб |
| 005 DQN Theory and Intuition - Part Three - Feedback and Function Approximation.mp4 |
88.34Мб |
| 005 Matplotlib - Figure Parameters.en.srt |
7.95Кб |
| 005 Matplotlib - Figure Parameters.mp4 |
23.77Мб |
| 005 MNIST Data Set Overview.en.srt |
7.65Кб |
| 005 MNIST Data Set Overview.mp4 |
24.75Мб |
| 005 Multi-Class Classification Considerations.en.srt |
16.84Кб |
| 005 Multi-Class Classification Considerations.mp4 |
46.07Мб |
| 005 Numpy Exercise Overview.en.srt |
2.15Кб |
| 005 Numpy Exercise Overview.mp4 |
11.54Мб |
| 005 OpenAI Gym - Working with the Environment.en.srt |
43.45Кб |
| 005 OpenAI Gym - Working with the Environment.mp4 |
137.50Мб |
| 005 Pandas - DataFrames - Part Two.en.srt |
13.77Кб |
| 005 Pandas - DataFrames - Part Two.mp4 |
54.04Мб |
| 005 Q-Learning Theory - Part Three - Q-Update Equation.en.srt |
11.66Кб |
| 005 Q-Learning Theory - Part Three - Q-Update Equation.mp4 |
37.52Мб |
| 005 Tabular Reinforcement Learning.html |
1.08Кб |
| 006 CNN on MNIST - The Data.en.srt |
19.42Кб |
| 006 CNN on MNIST - The Data.mp4 |
59.81Мб |
| 006 Cost Functions and Gradient Descent.en.srt |
28.73Кб |
| 006 Cost Functions and Gradient Descent.mp4 |
76.00Мб |
| 006 DQN Theory and Intuition - Part Four - Experience Replay.en.srt |
29.82Кб |
| 006 DQN Theory and Intuition - Part Four - Experience Replay.mp4 |
96.01Мб |
| 006 Matplotlib - Subplots Functionality.en.srt |
29.70Кб |
| 006 Matplotlib - Subplots Functionality.mp4 |
96.22Мб |
| 006 Numpy Exercise Solutions.en.srt |
11.29Кб |
| 006 Numpy Exercise Solutions.mp4 |
48.57Мб |
| 006 OpenAI Gym - Agent Interacting with the Environment.en.srt |
32.03Кб |
| 006 OpenAI Gym - Agent Interacting with the Environment.mp4 |
116.41Мб |
| 006 Pandas - DataFrames - Part Three.en.srt |
21.39Кб |
| 006 Pandas - DataFrames - Part Three.mp4 |
89.52Мб |
| 006 Q-Learning Theory - Part Four - Programmatic Q Updates.en.srt |
15.36Кб |
| 006 Q-Learning Theory - Part Four - Programmatic Q Updates.mp4 |
58.17Мб |
| 007 Backpropagation.en.srt |
21.84Кб |
| 007 Backpropagation.mp4 |
57.95Мб |
| 007 CNN on MNIST - Creating and Training the Model.en.srt |
26.01Кб |
| 007 CNN on MNIST - Creating and Training the Model.mp4 |
98.89Мб |
| 007 DQN Theory and Intuition - Part Five - Mapping Key Ideas to Code.en.srt |
24.04Кб |
| 007 DQN Theory and Intuition - Part Five - Mapping Key Ideas to Code.mp4 |
81.49Мб |
| 007 Matplotlib Styling - Legends.en.srt |
10.73Кб |
| 007 Matplotlib Styling - Legends.mp4 |
34.09Мб |
| 007 Pandas - DataFrames - Part Four.en.srt |
21.89Кб |
| 007 Pandas - DataFrames - Part Four.mp4 |
96.75Мб |
| 007 Q-Learning Implementation - Part One - Environment Setup.en.srt |
24.10Кб |
| 007 Q-Learning Implementation - Part One - Environment Setup.mp4 |
88.24Мб |
| 008 CNN on MNIST - Model Evaluation.en.srt |
10.10Кб |
| 008 CNN on MNIST - Model Evaluation.mp4 |
38.47Мб |
| 008 DQN Manual Implementation - Part One - Imports and Environment.en.srt |
7.85Кб |
| 008 DQN Manual Implementation - Part One - Imports and Environment.mp4 |
20.81Мб |
| 008 Matplotlib Styling - Colors and Styles.en.srt |
21.85Кб |
| 008 Matplotlib Styling - Colors and Styles.mp4 |
81.17Мб |
| 008 Q-Learning Implementation - Part Two - Table and Hyperparameters.en.srt |
17.27Кб |
| 008 Q-Learning Implementation - Part Two - Table and Hyperparameters.mp4 |
43.07Мб |
| 008 Scikit-Learn - Using Train-Test-Split.en.srt |
18.11Кб |
| 008 Scikit-Learn - Using Train-Test-Split.mp4 |
60.47Мб |
| 008 TensorFlow vs. Keras Explained.en.srt |
3.15Кб |
| 008 TensorFlow vs. Keras Explained.mp4 |
10.44Мб |
| 009 Advanced Matplotlib Commands (Optional).en.srt |
6.74Кб |
| 009 Advanced Matplotlib Commands (Optional).mp4 |
40.47Мб |
| 009 CNN on CIFAR-10 - The Data.en.srt |
18.12Кб |
| 009 CNN on CIFAR-10 - The Data.mp4 |
64.30Мб |
| 009 DQN Manual Implementation - Part Two - Artificial Neural Network.en.srt |
11.51Кб |
| 009 DQN Manual Implementation - Part Two - Artificial Neural Network.mp4 |
31.92Мб |
| 009 Keras Syntax - Preparing the Data.en.srt |
16.18Кб |
| 009 Keras Syntax - Preparing the Data.mp4 |
50.31Мб |
| 009 Q-Learning Implementation - Part Three - Update Functions.en.srt |
25.55Кб |
| 009 Q-Learning Implementation - Part Three - Update Functions.mp4 |
92.93Мб |
| 009 Scikit-Learn - Using Metrics.en.srt |
22.46Кб |
| 009 Scikit-Learn - Using Metrics.mp4 |
77.29Мб |
| 010 CNN on CIFAR-10 - Evaluating the Model.en.srt |
11.31Кб |
| 010 CNN on CIFAR-10 - Evaluating the Model.mp4 |
45.35Мб |
| 010 DQN Manual Implementation - Part Three - Hyperparameters and Functions.en.srt |
27.80Кб |
| 010 DQN Manual Implementation - Part Three - Hyperparameters and Functions.mp4 |
84.74Мб |
| 010 Keras Syntax - Creating and Training the Model.en.srt |
21.45Кб |
| 010 Keras Syntax - Creating and Training the Model.mp4 |
84.34Мб |
| 010 Matplotlib Exercise Questions Overview.en.srt |
9.68Кб |
| 010 Matplotlib Exercise Questions Overview.mp4 |
50.74Мб |
| 010 Q-Learning Implementation - Part Four - Agent Training.en.srt |
26.76Кб |
| 010 Q-Learning Implementation - Part Four - Agent Training.mp4 |
107.01Мб |
| 011 Downloading Data Set for Real Image Lectures.en.srt |
9.05Кб |
| 011 Downloading Data Set for Real Image Lectures.mp4 |
28.13Мб |
| 011 DQN Manual Implementation - Part Four - Model Training.en.srt |
25.66Кб |
| 011 DQN Manual Implementation - Part Four - Model Training.mp4 |
106.91Мб |
| 011 Keras Syntax - Model Evaluation.en.srt |
18.58Кб |
| 011 Keras Syntax - Model Evaluation.mp4 |
64.78Мб |
| 011 Matplotlib Exercise Questions - Solutions.en.srt |
25.55Кб |
| 011 Matplotlib Exercise Questions - Solutions.mp4 |
123.16Мб |
| 011 Q-Learning Implementation - Part Five - Visualization and Utilization.en.srt |
15.99Кб |
| 011 Q-Learning Implementation - Part Five - Visualization and Utilization.mp4 |
56.57Мб |
| 012 CNN on Real Image Files - Reading in the Data.en.srt |
21.73Кб |
| 012 CNN on Real Image Files - Reading in the Data.mp4 |
80.39Мб |
| 012 Continuous Q-Learning Theory - Part One - Environment Setup.en.srt |
20.96Кб |
| 012 Continuous Q-Learning Theory - Part One - Environment Setup.mp4 |
54.42Мб |
| 012 DQN - Keras-RL2 - Part One - Overview.en.srt |
11.76Кб |
| 012 DQN - Keras-RL2 - Part One - Overview.mp4 |
53.70Мб |
| 012 Keras Regression - Exploratory Data Analysis.en.srt |
27.98Кб |
| 012 Keras Regression - Exploratory Data Analysis.mp4 |
136.97Мб |
| 013 CNN on Real Image Files - Data Generation.en.srt |
24.33Кб |
| 013 CNN on Real Image Files - Data Generation.mp4 |
87.73Мб |
| 013 Continuous Q-Learning Theory - Part Two- Q-Table Shape.en.srt |
25.54Кб |
| 013 Continuous Q-Learning Theory - Part Two- Q-Table Shape.mp4 |
85.71Мб |
| 013 DQN - Keras-RL2 - Part Two - Imports and Environment.en.srt |
5.00Кб |
| 013 DQN - Keras-RL2 - Part Two - Imports and Environment.mp4 |
13.96Мб |
| 013 Keras Regression - EDA Continued.en.srt |
19.91Кб |
| 013 Keras Regression - EDA Continued.mp4 |
76.26Мб |
| 014 CNN on Real Image Files - Creating the Model.en.srt |
21.21Кб |
| 014 CNN on Real Image Files - Creating the Model.mp4 |
90.48Мб |
| 014 Continuous Q-Learning Theory - Part Three - Discretization Theory.en.srt |
7.82Кб |
| 014 Continuous Q-Learning Theory - Part Three - Discretization Theory.mp4 |
24.71Мб |
| 014 DQN - Keras-RL2 - Part Three - Creating the ANN.en.srt |
9.01Кб |
| 014 DQN - Keras-RL2 - Part Three - Creating the ANN.mp4 |
25.99Мб |
| 014 Keras Regression - Data Preprocessing and Model Creation.en.srt |
12.73Кб |
| 014 Keras Regression - Data Preprocessing and Model Creation.mp4 |
46.98Мб |
| 015 CNN on Real Image Files - Model Evaluation.en.srt |
12.92Кб |
| 015 CNN on Real Image Files - Model Evaluation.mp4 |
46.96Мб |
| 015 Continuous Q-Learning - Part Four - Discretization Implementation.en.srt |
27.03Кб |
| 015 Continuous Q-Learning - Part Four - Discretization Implementation.mp4 |
86.19Мб |
| 015 DQN - Keras-RL2 - Part Four - DQN Agent.en.srt |
23.13Кб |
| 015 DQN - Keras-RL2 - Part Four - DQN Agent.mp4 |
84.12Мб |
| 015 Keras Regression - Model Evaluation and Predictions.en.srt |
16.94Кб |
| 015 Keras Regression - Model Evaluation and Predictions.mp4 |
68.93Мб |
| 016 CNN Exercise Project Overview.en.srt |
3.99Кб |
| 016 CNN Exercise Project Overview.mp4 |
17.81Мб |
| 016 Continuous Q-Learning - Part Five - Functions and Hyperparameters.en.srt |
16.33Кб |
| 016 Continuous Q-Learning - Part Five - Functions and Hyperparameters.mp4 |
45.35Мб |
| 016 DQN - Exercise Overview.en.srt |
5.89Кб |
| 016 DQN - Exercise Overview.mp4 |
28.34Мб |
| 016 Keras Classification - EDA and Preprocessing.en.srt |
12.24Кб |
| 016 Keras Classification - EDA and Preprocessing.mp4 |
56.19Мб |
| 017 CNN Exercise Project Solutions.en.srt |
12.77Кб |
| 017 CNN Exercise Project Solutions.mp4 |
55.88Мб |
| 017 Continuous Q-Learning - Part Six - Training and Usage.en.srt |
31.74Кб |
| 017 Continuous Q-Learning - Part Six - Training and Usage.mp4 |
108.80Мб |
| 017 DQN - Exercise Solutions.en.srt |
15.45Кб |
| 017 DQN - Exercise Solutions.mp4 |
62.54Мб |
| 017 Keras Classification - Overfitting and Evaluation.en.srt |
25.32Кб |
| 017 Keras Classification - Overfitting and Evaluation.mp4 |
111.19Мб |
| 018 Keras Classification - Overview of Project Options.en.srt |
2.61Кб |
| 018 Keras Classification - Overview of Project Options.mp4 |
7.86Мб |
| 018 Q-Learning Exercise Project.en.srt |
12.45Кб |
| 018 Q-Learning Exercise Project.mp4 |
66.26Мб |
| 019 Keras Project Notebook Exercise Overview.en.srt |
13.11Кб |
| 019 Keras Project Notebook Exercise Overview.mp4 |
80.57Мб |
| 019 Q-Learning Exercise Project - Solutions.en.srt |
33.51Кб |
| 019 Q-Learning Exercise Project - Solutions.mp4 |
177.07Мб |
| 020 Keras Project Solution - Exploratoy Data Analysis.en.srt |
30.04Кб |
| 020 Keras Project Solution - Exploratoy Data Analysis.mp4 |
143.67Мб |
| 021 Keras Project Solutions - Missing Data - Part One.en.srt |
21.81Кб |
| 021 Keras Project Solutions - Missing Data - Part One.mp4 |
96.76Мб |
| 022 Keras Project Solutions - Dealing with Missing Data - Part Two.en.srt |
18.89Кб |
| 022 Keras Project Solutions - Dealing with Missing Data - Part Two.mp4 |
85.41Мб |
| 023 Keras Project Solutions - Categorical Data.en.srt |
27.17Кб |
| 023 Keras Project Solutions - Categorical Data.mp4 |
125.03Мб |
| 024 Keras Project Solutions - Data Preprocessing.en.srt |
5.45Кб |
| 024 Keras Project Solutions - Data Preprocessing.mp4 |
23.97Мб |
| 025 Keras Project Solutions- Creating and Training the Model.en.srt |
6.09Кб |
| 025 Keras Project Solutions- Creating and Training the Model.mp4 |
29.76Мб |
| 026 Keras Project Solutions - Model Evaluation.en.srt |
14.75Кб |
| 026 Keras Project Solutions - Model Evaluation.mp4 |
63.17Мб |
| 027 Tensorboard.en.srt |
30.65Кб |
| 027 Tensorboard.mp4 |
144.23Мб |
| 033 Advertising.csv |
4.11Кб |
| 110 DQNNaturePaper.pdf |
4.39Мб |
| external-assets-links.txt |
180б |