GigaCourse com Udemy Artificial Intelligence Reinforcement Learning in Python

GigaCourse com Udemy Artificial Intelligence Reinforcement Learning in Python

Name	[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python	DOWNLOAD Copy Link
Total Size	2.0 GB
Total Files	197
Last Seen	2024-11-15 23:37
Hash	ACD05B572F79A682C69B94DB38918B4E3E3110D7

/1. Welcome/
1. Introduction.mp4	35.9 MB
1. Introduction.srt	4.3 KB
2. Where to get the Code.mp4	4.7 MB
2. Where to get the Code.srt	5.5 KB
3. Strategy for Passing the Course.mp4	9.9 MB
3. Strategy for Passing the Course.srt	12.1 KB
4. Course Outline.mp4	32.5 MB
4. Course Outline.srt	7.0 KB
/10. Stock Trading Project with Reinforcement Learning/
1. Stock Trading Project Section Introduction.mp4	28.1 MB
1. Stock Trading Project Section Introduction.srt	7.0 KB
2. Data and Environment.mp4	54.5 MB
2. Data and Environment.srt	16.1 KB
3. How to Model Q for Q-Learning.mp4	47.1 MB
3. How to Model Q for Q-Learning.srt	12.3 KB
4. Design of the Program.mp4	24.4 MB
4. Design of the Program.srt	8.7 KB
5. Code pt 1.mp4	52.1 MB
5. Code pt 1.srt	9.9 KB
6. Code pt 2.mp4	68.5 MB
6. Code pt 2.srt	12.0 KB
7. Code pt 3.mp4	35.4 MB
7. Code pt 3.srt	5.5 KB
8. Code pt 4.mp4	51.5 MB
8. Code pt 4.srt	8.2 KB
9. Stock Trading Project Discussion.mp4	16.5 MB
9. Stock Trading Project Discussion.srt	4.5 KB
/11. Appendix FAQ/
1. What is the Appendix.mp4	5.7 MB
1. What is the Appendix.srt	3.8 KB
10. What order should I take your courses in (part 1).mp4	30.7 MB
10. What order should I take your courses in (part 1).srt	16.4 KB
11. What order should I take your courses in (part 2).mp4	39.4 MB
11. What order should I take your courses in (part 2).srt	23.6 KB
12. BONUS Where to get discount coupons and FREE deep learning material.mp4	39.7 MB
12. BONUS Where to get discount coupons and FREE deep learning material.srt	8.1 KB
2. Windows-Focused Environment Setup 2018.mp4	195.4 MB
2. Windows-Focused Environment Setup 2018.srt	20.6 KB
3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	46.0 MB
3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt	18.8 KB
4. How to Code by Yourself (part 1).mp4	25.7 MB
4. How to Code by Yourself (part 1).srt	30.9 KB
5. How to Code by Yourself (part 2).mp4	15.5 MB
5. How to Code by Yourself (part 2).srt	18.9 KB
6. How to Succeed in this Course (Long Version).mp4	19.2 MB
6. How to Succeed in this Course (Long Version).srt	14.9 KB
7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	40.8 MB
7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt	32.5 KB
8. Proof that using Jupyter Notebook is the same as not using it.mp4	82.1 MB
8. Proof that using Jupyter Notebook is the same as not using it.srt	14.5 KB
9. Python 2 vs Python 3.mp4	8.2 MB
9. Python 2 vs Python 3.srt	6.2 KB
/2. Return of the Multi-Armed Bandit/
1. Problem Setup and The Explore-Exploit Dilemma.mp4	6.8 MB
1. Problem Setup and The Explore-Exploit Dilemma.srt	8.0 KB
10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4	11.1 MB
10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt	6.2 KB
11. Nonstationary Bandits.mp4	7.8 MB
11. Nonstationary Bandits.srt	8.0 KB
12. Bandit Summary, Real Data, and Online Learning.mp4	35.6 MB
12. Bandit Summary, Real Data, and Online Learning.srt	9.3 KB
2. Applications of the Explore-Exploit Dilemma.mp4	53.7 MB
2. Applications of the Explore-Exploit Dilemma.srt	11.2 KB
3. Epsilon-Greedy.mp4	2.9 MB
3. Epsilon-Greedy.srt	3.3 KB
4. Updating a Sample Mean.mp4	2.3 MB
4. Updating a Sample Mean.srt	2.2 KB
5. Designing Your Bandit Program.mp4	25.7 MB
5. Designing Your Bandit Program.srt	5.7 KB
6. Comparing Different Epsilons.mp4	8.4 MB
6. Comparing Different Epsilons.srt	5.4 KB
7. Optimistic Initial Values.mp4	16.6 MB
7. Optimistic Initial Values.srt	3.1 KB
8. UCB1.mp4	8.6 MB
8. UCB1.srt	8.3 KB
9. Bayesian Thompson Sampling.mp4	54.4 MB
9. Bayesian Thompson Sampling.srt	12.1 KB
/3. High Level Overview of Reinforcement Learning/
1. What is Reinforcement Learning.mp4	57.3 MB
1. What is Reinforcement Learning.srt	11.2 KB
2. On Unusual or Unexpected Strategies of RL.mp4	38.9 MB
2. On Unusual or Unexpected Strategies of RL.srt	8.1 KB
3. Defining Some Terms.mp4	44.4 MB
3. Defining Some Terms.srt	9.4 KB
/4. Build an Intelligent Tic-Tac-Toe Agent/
1. Naive Solution to Tic-Tac-Toe.mp4	6.4 MB
1. Naive Solution to Tic-Tac-Toe.srt	7.4 KB
10. Tic Tac Toe Code Main Loop and Demo.mp4	9.9 MB
10. Tic Tac Toe Code Main Loop and Demo.srt	9.5 KB
11. Tic Tac Toe Summary.mp4	8.7 MB
11. Tic Tac Toe Summary.srt	10.5 KB
12. Tic Tac Toe Exercise.mp4	20.7 MB
12. Tic Tac Toe Exercise.srt	4.7 KB
2. Components of a Reinforcement Learning System.mp4	13.3 MB
2. Components of a Reinforcement Learning System.srt	15.2 KB
3. Notes on Assigning Rewards.mp4	4.4 MB
3. Notes on Assigning Rewards.srt	5.1 KB
4. The Value Function and Your First Reinforcement Learning Algorithm.mp4	108.8 MB
4. The Value Function and Your First Reinforcement Learning Algorithm.srt	23.3 KB
5. Tic Tac Toe Code Outline.mp4	5.3 MB
5. Tic Tac Toe Code Outline.srt	6.6 KB
6. Tic Tac Toe Code Representing States.mp4	4.6 MB
6. Tic Tac Toe Code Representing States.srt	5.0 KB
7. Tic Tac Toe Code Enumerating States Recursively.mp4	10.3 MB
7. Tic Tac Toe Code Enumerating States Recursively.srt	11.6 KB
8. Tic Tac Toe Code The Environment.mp4	10.5 MB
8. Tic Tac Toe Code The Environment.srt	12.3 KB
9. Tic Tac Toe Code The Agent.mp4	9.4 MB
9. Tic Tac Toe Code The Agent.srt	11.2 KB
/5. Markov Decision Proccesses/
1. Gridworld.mp4	3.5 MB
1. Gridworld.srt	4.1 KB
2. The Markov Property.mp4	7.5 MB
2. The Markov Property.srt	8.6 KB
3. Defining and Formalizing the MDP.mp4	7.0 MB
3. Defining and Formalizing the MDP.srt	8.1 KB
4. Future Rewards.mp4	5.4 MB
4. Future Rewards.srt	6.2 KB
5. Value Function Introduction.mp4	20.7 MB
5. Value Function Introduction.srt	16.0 KB
6. Value Functions.mp4	8.7 MB
6. Value Functions.srt	12.0 KB
7. Bellman Examples.mp4	91.3 MB
7. Bellman Examples.srt	28.3 KB
8. Optimal Policy and Optimal Value Function.mp4	3.4 MB
8. Optimal Policy and Optimal Value Function.srt	5.1 KB
9. MDP Summary.mp4	5.9 MB
9. MDP Summary.srt	2.0 KB
/6. Dynamic Programming/
1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4	5.1 MB
1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt	5.5 KB
10. Value Iteration in Code.mp4	5.1 MB
10. Value Iteration in Code.srt	3.4 KB
11. Dynamic Programming Summary.mp4	8.7 MB
11. Dynamic Programming Summary.srt	9.6 KB
2. Gridworld in Code.mp4	12.0 MB
2. Gridworld in Code.srt	11.2 KB
3. Designing Your RL Program.mp4	23.4 MB
3. Designing Your RL Program.srt	6.8 KB
4. Iterative Policy Evaluation in Code.mp4	12.6 MB
4. Iterative Policy Evaluation in Code.srt	10.5 KB
5. Policy Improvement.mp4	4.8 MB
5. Policy Improvement.srt	5.3 KB
6. Policy Iteration.mp4	3.3 MB
6. Policy Iteration.srt	3.5 KB
7. Policy Iteration in Code.mp4	8.0 MB
7. Policy Iteration in Code.srt	6.2 KB
8. Policy Iteration in Windy Gridworld.mp4	9.5 MB
8. Policy Iteration in Windy Gridworld.srt	8.4 KB
9. Value Iteration.mp4	6.5 MB
9. Value Iteration.srt	7.1 KB
/7. Monte Carlo/
1. Monte Carlo Intro.mp4	5.2 MB
1. Monte Carlo Intro.srt	6.1 KB
2. Monte Carlo Policy Evaluation.mp4	9.2 MB
2. Monte Carlo Policy Evaluation.srt	11.1 KB
3. Monte Carlo Policy Evaluation in Code.mp4	8.3 MB
3. Monte Carlo Policy Evaluation in Code.srt	6.3 KB
4. Policy Evaluation in Windy Gridworld.mp4	8.2 MB
4. Policy Evaluation in Windy Gridworld.srt	5.4 KB
5. Monte Carlo Control.mp4	9.7 MB
5. Monte Carlo Control.srt	10.5 KB
6. Monte Carlo Control in Code.mp4	10.7 MB
6. Monte Carlo Control in Code.srt	6.0 KB
7. Monte Carlo Control without Exploring Starts.mp4	4.8 MB
7. Monte Carlo Control without Exploring Starts.srt	5.7 KB
8. Monte Carlo Control without Exploring Starts in Code.mp4	8.4 MB
8. Monte Carlo Control without Exploring Starts in Code.srt	3.7 KB
9. Monte Carlo Summary.mp4	6.0 MB
9. Monte Carlo Summary.srt	7.3 KB
/8. Temporal Difference Learning/
1. Temporal Difference Intro.mp4	2.9 MB
1. Temporal Difference Intro.srt	3.4 KB
2. TD(0) Prediction.mp4	6.1 MB
2. TD(0) Prediction.srt	6.5 KB
3. TD(0) Prediction in Code.mp4	5.6 MB
3. TD(0) Prediction in Code.srt	4.1 KB
4. SARSA.mp4	8.6 MB
4. SARSA.srt	9.9 KB
5. SARSA in Code.mp4	9.2 MB
5. SARSA in Code.srt	5.7 KB
6. Q Learning.mp4	5.1 MB
6. Q Learning.srt	6.0 KB
7. Q Learning in Code.mp4	5.7 MB
7. Q Learning in Code.srt	3.5 KB
8. TD Summary.mp4	4.1 MB
8. TD Summary.srt	4.8 KB
/9. Approximation Methods/
1. Approximation Intro.mp4	6.8 MB
1. Approximation Intro.srt	8.2 KB
2. Linear Models for Reinforcement Learning.mp4	6.8 MB
2. Linear Models for Reinforcement Learning.srt	7.6 KB
3. Features.mp4	6.6 MB
3. Features.srt	7.1 KB
4. Monte Carlo Prediction with Approximation.mp4	3.0 MB
4. Monte Carlo Prediction with Approximation.srt	2.4 KB
5. Monte Carlo Prediction with Approximation in Code.mp4	6.9 MB
5. Monte Carlo Prediction with Approximation in Code.srt	4.1 KB
6. TD(0) Semi-Gradient Prediction.mp4	8.8 MB
6. TD(0) Semi-Gradient Prediction.srt	6.5 KB
7. Semi-Gradient SARSA.mp4	4.9 MB
7. Semi-Gradient SARSA.srt	5.6 KB
8. Semi-Gradient SARSA in Code.mp4	11.1 MB
8. Semi-Gradient SARSA in Code.srt	5.5 KB
9. Course Summary and Next Steps.mp4	13.9 MB
9. Course Summary and Next Steps.srt	16.3 KB
/
[GigaCourse.com].url	0.0 KB
Total files 197