Artificial Intelligence Reinforcement Learning in Python

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4.14 GB | 20min 20s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 -Introduction.mp4 (34.24 MB)
2 -Course Outline and Big Picture.mp4 (39.68 MB)
3 -Where to get the Code.mp4 (22.72 MB)
4 -How to Succeed in this Course.mp4 (16.27 MB)
5 -Warmup.mp4 (62.6 MB)
1 -Section Introduction The Explore-Exploit Dilemma.mp4 (51.99 MB)
2 -Applications of the Explore-Exploit Dilemma.mp4 (51.18 MB)
3 -Epsilon-Greedy Theory.mp4 (28.3 MB)
4 -Calculating a Sample Mean (pt 1).mp4 (23.13 MB)
5 -Epsilon-Greedy Beginner's Exercise Prompt.mp4 (28.66 MB)
6 -Designing Your Bandit Program.mp4 (24.51 MB)
7 -Epsilon-Greedy in Code.mp4 (41.43 MB)
8 -Comparing Different Epsilons.mp4 (43.65 MB)
9 -Optimistic Initial Values Theory.mp4 (23.52 MB)
10 -Optimistic Initial Values Beginner's Exercise Prompt.mp4 (13.77 MB)
11 -Optimistic Initial Values Code.mp4 (24.57 MB)
12 -UCB1 Theory.mp4 (55.53 MB)
13 -UCB1 Beginner's Exercise Prompt.mp4 (12.74 MB)
14 -UCB1 Code.mp4 (20.66 MB)
15 -Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 (55.9 MB)
16 -Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 (74.5 MB)
17 -Thompson Sampling Beginner's Exercise Prompt.mp4 (17.89 MB)
18 -Thompson Sampling Code.mp4 (32.83 MB)
19 -Thompson Sampling With Gaussian Reward Theory.mp4 (48.51 MB)
20 -Thompson Sampling With Gaussian Reward Code.mp4 (43.43 MB)
21 -Exercise on Gaussian Rewards.mp4 (6.82 MB)
22 -Why don't we just use a library.mp4 (27.4 MB)
23 -Nonstationary Bandits.mp4 (30.98 MB)
24 -Bandit Summary, Real Data, and Online Learning.mp4 (34.61 MB)
25 -(Optional) Alternative Bandit Designs.mp4 (50.34 MB)
26 -Suggestion Box.mp4 (27.17 MB)
1 -What is Reinforcement Learning.mp4 (54.62 MB)
2 -From Bandits to Full Reinforcement Learning.mp4 (41.19 MB)
1 -MDP Section Introduction.mp4 (37.2 MB)
2 -Gridworld.mp4 (53.99 MB)
3 -Choosing Rewards.mp4 (32.49 MB)
4 -The Markov Property.mp4 (21.76 MB)
5 -Markov Decision Processes (MDPs).mp4 (61.73 MB)
6 -Future Rewards.mp4 (39.5 MB)
7 -Value Functions.mp4 (18.55 MB)
8 -The Bellman Equation (pt 1).mp4 (27.78 MB)
9 -The Bellman Equation (pt 2).mp4 (26.69 MB)
10 -The Bellman Equation (pt 3).mp4 (24.67 MB)
11 -Bellman Examples.mp4 (87.12 MB)
12 -Optimal Policy and Optimal Value Function (pt 1).mp4 (56.06 MB)
13 -Optimal Policy and Optimal Value Function (pt 2).mp4 (18.72 MB)
14 -MDP Summary.mp4 (14.28 MB)
1 -Dynamic Programming Section Introduction.mp4 (34.67 MB)
2 -Iterative Policy Evaluation.mp4 (60.75 MB)
3 -Designing Your RL Program.mp4 (22.34 MB)
4 -Gridworld in Code.mp4 (46.79 MB)
5 -Iterative Policy Evaluation in Code.mp4 (68.43 MB)
6 -Windy Gridworld in Code.mp4 (41.45 MB)
7 -Iterative Policy Evaluation for Windy Gridworld in Code.mp4 (46.93 MB)
8 -Policy Improvement.mp4 (43.99 MB)
9 -Policy Iteration.mp4 (34.15 MB)
10 -Policy Iteration in Code.mp4 (56.38 MB)
11 -Policy Iteration in Windy Gridworld.mp4 (51.41 MB)
12 -Value Iteration.mp4 (35.27 MB)
13 -Value Iteration in Code.mp4 (45.67 MB)
14 -Dynamic Programming Summary.mp4 (25.11 MB)
1 -Monte Carlo Intro.mp4 (47.59 MB)
2 -Monte Carlo Policy Evaluation.mp4 (47.15 MB)
3 -Monte Carlo Policy Evaluation in Code.mp4 (51.65 MB)
4 -Monte Carlo Control.mp4 (35.61 MB)
5 -Monte Carlo Control in Code.mp4 (64.41 MB)
6 -Monte Carlo Control without Exploring Starts.mp4 (23.4 MB)
7 -Monte Carlo Control without Exploring Starts in Code.mp4 (40.69 MB)
8 -Monte Carlo Summary.mp4 (11.4 MB)
1 -Temporal Difference Introduction.mp4 (14.44 MB)
2 -TD(0) Prediction.mp4 (15.79 MB)
3 -TD(0) Prediction in Code.mp4 (32.43 MB)
4 -SARSA.mp4 (16.22 MB)
5 -SARSA in Code.mp4 (44.9 MB)
6 -Q Learning.mp4 (19.82 MB)
7 -Q Learning in Code.mp4 (38.55 MB)
8 -TD Learning Section Summary.mp4 (10.04 MB)
1 -Approximation Methods Section Introduction.mp4 (22.08 MB)
2 -Linear Models for Reinforcement Learning.mp4 (31.08 MB)
3 -Feature Engineering.mp4 (45.88 MB)
4 -Approximation Methods for Prediction.mp4 (34.34 MB)
5 -Approximation Methods for Prediction Code.mp4 (62.29 MB)
6 -Approximation Methods for Control.mp4 (17.59 MB)
7 -Approximation Methods for Control Code.mp4 (77.69 MB)
8 -CartPole.mp4 (26.9 MB)
9 -CartPole Code.mp4 (48.19 MB)
10 -Approximation Methods Exercise.mp4 (17.53 MB)
11 -Approximation Methods Section Summary.mp4 (21.75 MB)
1 -This Course vs RL Book What's the Difference.mp4 (38.21 MB)
1 -Beginners, halt! Stop here if you skipped ahead.mp4 (83.78 MB)
2 -Stock Trading Project Section Introduction.mp4 (26.76 MB)
3 -Data and Environment.mp4 (52.01 MB)
4 -How to Model Q for Q-Learning.mp4 (44.89 MB)
5 -Design of the Program.mp4 (23.31 MB)
6 -Code pt 1.mp4 (49.72 MB)
7 -Code pt 2.mp4 (65.29 MB)
8 -Code pt 3.mp4 (33.72 MB)
9 -Code pt 4.mp4 (52.94 MB)
10 -Stock Trading Project Discussion.mp4 (15.78 MB)
1 -Pre-Installation Check.mp4 (22.74 MB)
2 -Anaconda Environment Setup.mp4 (186.38 MB)
3 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.92 MB)
1 -How to Code by Yourself (part 1).mp4 (24.53 MB)
2 -How to Code by Yourself (part 2).mp4 (14.8 MB)
3 -Proof that using Jupyter Notebook is the same as not using it.mp4 (78.32 MB)
4 -Python 2 vs Python 3.mp4 (7.83 MB)
1 -How to Succeed in this Course (Long Version).mp4 (18.31 MB)
2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (38.95 MB)
3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (29.32 MB)
4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (37.62 MB)
1 -What is the Appendix.mp4 (5.45 MB)
2 -BONUS.mp4 (40.5 MB)]
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