Udemy - Artificial Intelligence Reinforcement Learning in Python (12.2024)

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Udemy - Artificial Intelligence Reinforcement Learning in Python (12.2024)

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收录时间：2025-07-06
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文件列表

  • 11 - Setting Up Your Environment (FAQ by Student Request)/2 -Anaconda Environment Setup.mp4 195.4 MB
  • 4 - Markov Decision Proccesses/11 -Bellman Examples.mp4 91.3 MB
  • 10 - Stock Trading Project with Reinforcement Learning/1 -Beginners, halt! Stop here if you skipped ahead.mp4 87.8 MB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/3 -Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 8 - Approximation Methods/7 -Approximation Methods for Control Code.mp4 81.5 MB
  • 2 - Return of the Multi-Armed Bandit/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 78.1 MB
  • 5 - Dynamic Programming/5 -Iterative Policy Evaluation in Code.mp4 71.8 MB
  • 10 - Stock Trading Project with Reinforcement Learning/7 -Code pt 2.mp4 68.5 MB
  • 6 - Monte Carlo/5 -Monte Carlo Control in Code.mp4 67.5 MB
  • 1 - Welcome/5 -Warmup.mp4 65.6 MB
  • 8 - Approximation Methods/5 -Approximation Methods for Prediction Code.mp4 65.3 MB
  • 4 - Markov Decision Proccesses/5 -Markov Decision Processes (MDPs).mp4 64.7 MB
  • 5 - Dynamic Programming/2 -Iterative Policy Evaluation.mp4 63.7 MB
  • 5 - Dynamic Programming/10 -Policy Iteration in Code.mp4 59.1 MB
  • 4 - Markov Decision Proccesses/12 -Optimal Policy and Optimal Value Function (pt 1).mp4 58.8 MB
  • 2 - Return of the Multi-Armed Bandit/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 58.6 MB
  • 2 - Return of the Multi-Armed Bandit/12 -UCB1 Theory.mp4 58.2 MB
  • 3 - High Level Overview of Reinforcement Learning/1 -What is Reinforcement Learning.mp4 57.3 MB
  • 4 - Markov Decision Proccesses/2 -Gridworld.mp4 56.6 MB
  • 10 - Stock Trading Project with Reinforcement Learning/9 -Code pt 4.mp4 55.5 MB
  • 10 - Stock Trading Project with Reinforcement Learning/3 -Data and Environment.mp4 54.5 MB
  • 2 - Return of the Multi-Armed Bandit/1 -Section Introduction The Explore-Exploit Dilemma.mp4 54.5 MB
  • 6 - Monte Carlo/3 -Monte Carlo Policy Evaluation in Code.mp4 54.2 MB
  • 5 - Dynamic Programming/11 -Policy Iteration in Windy Gridworld.mp4 53.9 MB
  • 2 - Return of the Multi-Armed Bandit/2 -Applications of the Explore-Exploit Dilemma.mp4 53.7 MB
  • 2 - Return of the Multi-Armed Bandit/25 -(Optional) Alternative Bandit Designs.mp4 52.8 MB
  • 10 - Stock Trading Project with Reinforcement Learning/6 -Code pt 1.mp4 52.1 MB
  • 2 - Return of the Multi-Armed Bandit/19 -Thompson Sampling With Gaussian Reward Theory.mp4 50.9 MB
  • 8 - Approximation Methods/9 -CartPole Code.mp4 50.5 MB
  • 6 - Monte Carlo/1 -Monte Carlo Intro.mp4 49.9 MB
  • 6 - Monte Carlo/2 -Monte Carlo Policy Evaluation.mp4 49.4 MB
  • 5 - Dynamic Programming/7 -Iterative Policy Evaluation for Windy Gridworld in Code.mp4 49.2 MB
  • 5 - Dynamic Programming/4 -Gridworld in Code.mp4 49.1 MB
  • 8 - Approximation Methods/3 -Feature Engineering.mp4 48.1 MB
  • 5 - Dynamic Programming/13 -Value Iteration in Code.mp4 47.9 MB
  • 7 - Temporal Difference Learning/5 -SARSA in Code.mp4 47.1 MB
  • 10 - Stock Trading Project with Reinforcement Learning/4 -How to Model Q for Q-Learning.mp4 47.1 MB
  • 5 - Dynamic Programming/8 -Policy Improvement.mp4 46.1 MB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/3 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 2 - Return of the Multi-Armed Bandit/8 -Comparing Different Epsilons.mp4 45.8 MB
  • 2 - Return of the Multi-Armed Bandit/20 -Thompson Sampling With Gaussian Reward Code.mp4 45.5 MB
  • 5 - Dynamic Programming/6 -Windy Gridworld in Code.mp4 43.5 MB
  • 2 - Return of the Multi-Armed Bandit/7 -Epsilon-Greedy in Code.mp4 43.4 MB
  • 3 - High Level Overview of Reinforcement Learning/2 -From Bandits to Full Reinforcement Learning.mp4 43.2 MB
  • 6 - Monte Carlo/7 -Monte Carlo Control without Exploring Starts in Code.mp4 42.7 MB
  • 14 - Appendix FAQ Finale/2 -BONUS.mp4 42.5 MB
  • 1 - Welcome/2 -Course Outline and Big Picture.mp4 41.6 MB
  • 4 - Markov Decision Proccesses/6 -Future Rewards.mp4 41.4 MB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 7 - Temporal Difference Learning/7 -Q Learning in Code.mp4 40.4 MB
  • 9 - Interlude Common Beginner Questions/1 -This Course vs. RL Book What's the Difference.mp4 40.1 MB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
  • 4 - Markov Decision Proccesses/1 -MDP Section Introduction.mp4 39.0 MB
  • 6 - Monte Carlo/4 -Monte Carlo Control.mp4 37.3 MB
  • 5 - Dynamic Programming/12 -Value Iteration.mp4 37.0 MB
  • 5 - Dynamic Programming/1 -Dynamic Programming Section Introduction.mp4 36.4 MB
  • 2 - Return of the Multi-Armed Bandit/24 -Bandit Summary, Real Data, and Online Learning.mp4 36.3 MB
  • 8 - Approximation Methods/4 -Approximation Methods for Prediction.mp4 36.0 MB
  • 1 - Welcome/1 -Introduction.mp4 35.9 MB
  • 5 - Dynamic Programming/9 -Policy Iteration.mp4 35.8 MB
  • 10 - Stock Trading Project with Reinforcement Learning/8 -Code pt 3.mp4 35.4 MB
  • 2 - Return of the Multi-Armed Bandit/18 -Thompson Sampling Code.mp4 34.4 MB
  • 4 - Markov Decision Proccesses/3 -Choosing Rewards.mp4 34.1 MB
  • 7 - Temporal Difference Learning/3 -TD(0) Prediction in Code.mp4 34.0 MB
  • 8 - Approximation Methods/2 -Linear Models for Reinforcement Learning.mp4 32.6 MB
  • 2 - Return of the Multi-Armed Bandit/23 -Nonstationary Bandits.mp4 32.5 MB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
  • 2 - Return of the Multi-Armed Bandit/5 -Epsilon-Greedy Beginner's Exercise Prompt.mp4 30.1 MB
  • 2 - Return of the Multi-Armed Bandit/3 -Epsilon-Greedy Theory.mp4 29.7 MB
  • 4 - Markov Decision Proccesses/8 -The Bellman Equation (pt 1).mp4 29.1 MB
  • 2 - Return of the Multi-Armed Bandit/22 -Why don't we just use a library.mp4 28.7 MB
  • 2 - Return of the Multi-Armed Bandit/26 -Suggestion Box.mp4 28.5 MB
  • 8 - Approximation Methods/8 -CartPole.mp4 28.2 MB
  • 10 - Stock Trading Project with Reinforcement Learning/2 -Stock Trading Project Section Introduction.mp4 28.1 MB
  • 4 - Markov Decision Proccesses/9 -The Bellman Equation (pt 2).mp4 28.0 MB
  • 5 - Dynamic Programming/14 -Dynamic Programming Summary.mp4 26.3 MB
  • 4 - Markov Decision Proccesses/10 -The Bellman Equation (pt 3).mp4 25.9 MB
  • 2 - Return of the Multi-Armed Bandit/11 -Optimistic Initial Values Code.mp4 25.8 MB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -How to Code by Yourself (part 1).mp4 25.7 MB
  • 2 - Return of the Multi-Armed Bandit/6 -Designing Your Bandit Program.mp4 25.7 MB
  • 2 - Return of the Multi-Armed Bandit/9 -Optimistic Initial Values Theory.mp4 24.7 MB
  • 6 - Monte Carlo/6 -Monte Carlo Control without Exploring Starts.mp4 24.5 MB
  • 10 - Stock Trading Project with Reinforcement Learning/5 -Design of the Program.mp4 24.4 MB
  • 2 - Return of the Multi-Armed Bandit/4 -Calculating a Sample Mean (pt 1).mp4 24.3 MB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/1 -Pre-Installation Check.mp4 23.8 MB
  • 1 - Welcome/3 -Where to get the Code.mp4 23.8 MB
  • 5 - Dynamic Programming/3 -Designing Your RL Program.mp4 23.4 MB
  • 8 - Approximation Methods/1 -Approximation Methods Section Introduction.mp4 23.1 MB
  • 4 - Markov Decision Proccesses/4 -The Markov Property.mp4 22.8 MB
  • 8 - Approximation Methods/11 -Approximation Methods Section Summary.mp4 22.8 MB
  • 2 - Return of the Multi-Armed Bandit/14 -UCB1 Code.mp4 21.7 MB
  • 7 - Temporal Difference Learning/6 -Q Learning.mp4 20.8 MB
  • 4 - Markov Decision Proccesses/13 -Optimal Policy and Optimal Value Function (pt 2).mp4 19.6 MB
  • 4 - Markov Decision Proccesses/7 -Value Functions.mp4 19.5 MB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1 -How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 2 - Return of the Multi-Armed Bandit/17 -Thompson Sampling Beginner's Exercise Prompt.mp4 18.8 MB
  • 8 - Approximation Methods/6 -Approximation Methods for Control.mp4 18.4 MB
  • 8 - Approximation Methods/10 -Approximation Methods Exercise.mp4 18.4 MB
  • 1 - Welcome/4 -How to Succeed in this Course.mp4 17.1 MB
  • 7 - Temporal Difference Learning/4 -SARSA.mp4 17.0 MB
  • 7 - Temporal Difference Learning/2 -TD(0) Prediction.mp4 16.6 MB
  • 10 - Stock Trading Project with Reinforcement Learning/10 -Stock Trading Project Discussion.mp4 16.5 MB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code by Yourself (part 2).mp4 15.5 MB
  • 7 - Temporal Difference Learning/1 -Temporal Difference Introduction.mp4 15.1 MB
  • 4 - Markov Decision Proccesses/14 -MDP Summary.mp4 15.0 MB
  • 2 - Return of the Multi-Armed Bandit/10 -Optimistic Initial Values Beginner's Exercise Prompt.mp4 14.4 MB
  • 2 - Return of the Multi-Armed Bandit/13 -UCB1 Beginner's Exercise Prompt.mp4 13.4 MB
  • 6 - Monte Carlo/8 -Monte Carlo Summary.mp4 12.0 MB
  • 7 - Temporal Difference Learning/8 -TD Learning Section Summary.mp4 10.5 MB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/4 -Python 2 vs Python 3.mp4 8.2 MB
  • 2 - Return of the Multi-Armed Bandit/21 -Exercise on Gaussian Rewards.mp4 7.2 MB
  • 14 - Appendix FAQ Finale/1 -What is the Appendix.mp4 5.7 MB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.fr_FR.vtt 32.5 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.de_DE.vtt 31.6 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.pt_BR.vtt 30.6 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.es_ES.vtt 30.5 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.it_IT.vtt 30.3 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28.4 kB
  • 4 - Markov Decision Proccesses/subtitles/11 -Bellman Examples.fr_FR.vtt 28.1 kB
  • 4 - Markov Decision Proccesses/subtitles/11 -Bellman Examples.de_DE.vtt 28.0 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -How to Code by Yourself (part 1).vtt 28.0 kB
  • 4 - Markov Decision Proccesses/subtitles/11 -Bellman Examples.es_ES.vtt 26.4 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/1 -How to Code by Yourself (part 1).fr_FR.vtt 26.3 kB
  • 4 - Markov Decision Proccesses/subtitles/11 -Bellman Examples.pt_BR.vtt 26.2 kB
  • 4 - Markov Decision Proccesses/subtitles/11 -Bellman Examples.it_IT.vtt 26.2 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/1 -How to Code by Yourself (part 1).de_DE.vtt 25.3 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/1 -How to Code by Yourself (part 1).pt_BR.vtt 24.8 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/1 -How to Code by Yourself (part 1).es_ES.vtt 24.7 kB
  • 4 - Markov Decision Proccesses/11 -Bellman Examples.vtt 24.7 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).fr_FR.vtt 24.5 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).fr_FR.vtt 24.4 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/1 -How to Code by Yourself (part 1).it_IT.vtt 24.3 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).de_DE.vtt 23.4 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).de_DE.vtt 23.1 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).es_ES.vtt 22.9 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).pt_BR.vtt 22.8 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).it_IT.vtt 22.7 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).it_IT.vtt 22.7 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).es_ES.vtt 22.6 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).pt_BR.vtt 22.3 kB
  • 5 - Dynamic Programming/subtitles/2 -Iterative Policy Evaluation.fr_FR.vtt 21.7 kB
  • 2 - Return of the Multi-Armed Bandit/16 -Bayesian Bandits Thompson Sampling Theory (pt 2).vtt 21.2 kB
  • 5 - Dynamic Programming/subtitles/2 -Iterative Policy Evaluation.de_DE.vtt 21.0 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/1 -Beginners, halt! Stop here if you skipped ahead.fr_FR.vtt 20.9 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/2 -Anaconda Environment Setup.fr_FR.vtt 20.8 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.7 kB
  • 5 - Dynamic Programming/subtitles/2 -Iterative Policy Evaluation.es_ES.vtt 20.6 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/12 -UCB1 Theory.fr_FR.vtt 20.4 kB
  • 5 - Dynamic Programming/subtitles/2 -Iterative Policy Evaluation.it_IT.vtt 20.3 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/2 -Anaconda Environment Setup.de_DE.vtt 20.2 kB
  • 5 - Dynamic Programming/subtitles/2 -Iterative Policy Evaluation.pt_BR.vtt 20.2 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/1 -Beginners, halt! Stop here if you skipped ahead.de_DE.vtt 20.1 kB
  • 4 - Markov Decision Proccesses/subtitles/5 -Markov Decision Processes (MDPs).fr_FR.vtt 20.1 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/12 -UCB1 Theory.de_DE.vtt 20.0 kB
  • 4 - Markov Decision Proccesses/subtitles/5 -Markov Decision Processes (MDPs).de_DE.vtt 19.9 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/1 -Beginners, halt! Stop here if you skipped ahead.es_ES.vtt 19.6 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/1 -Beginners, halt! Stop here if you skipped ahead.pt_BR.vtt 19.5 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/2 -Anaconda Environment Setup.es_ES.vtt 19.4 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/1 -Beginners, halt! Stop here if you skipped ahead.it_IT.vtt 19.4 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/12 -UCB1 Theory.it_IT.vtt 19.2 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/12 -UCB1 Theory.es_ES.vtt 19.1 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/2 -Anaconda Environment Setup.pt_BR.vtt 19.1 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/2 -Anaconda Environment Setup.it_IT.vtt 19.1 kB
  • 1 - Welcome/subtitles/5 -Warmup.fr_FR.vtt 18.9 kB
  • 5 - Dynamic Programming/2 -Iterative Policy Evaluation.vtt 18.9 kB
  • 4 - Markov Decision Proccesses/subtitles/5 -Markov Decision Processes (MDPs).es_ES.vtt 18.9 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/12 -UCB1 Theory.pt_BR.vtt 18.8 kB
  • 4 - Markov Decision Proccesses/subtitles/5 -Markov Decision Processes (MDPs).it_IT.vtt 18.7 kB
  • 4 - Markov Decision Proccesses/subtitles/5 -Markov Decision Processes (MDPs).pt_BR.vtt 18.5 kB
  • 1 - Welcome/subtitles/5 -Warmup.de_DE.vtt 18.5 kB
  • 10 - Stock Trading Project with Reinforcement Learning/1 -Beginners, halt! Stop here if you skipped ahead.vtt 18.4 kB
  • 2 - Return of the Multi-Armed Bandit/12 -UCB1 Theory.vtt 17.9 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/2 -Anaconda Environment Setup.vtt 17.8 kB
  • 1 - Welcome/subtitles/5 -Warmup.es_ES.vtt 17.6 kB
  • 4 - Markov Decision Proccesses/5 -Markov Decision Processes (MDPs).vtt 17.6 kB
  • 1 - Welcome/subtitles/5 -Warmup.it_IT.vtt 17.6 kB
  • 4 - Markov Decision Proccesses/subtitles/2 -Gridworld.fr_FR.vtt 17.5 kB
  • 1 - Welcome/subtitles/5 -Warmup.pt_BR.vtt 17.4 kB
  • 4 - Markov Decision Proccesses/subtitles/2 -Gridworld.de_DE.vtt 17.4 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).fr_FR.vtt 17.2 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code by Yourself (part 2).vtt 17.1 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/3 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 17.0 kB
  • 1 - Welcome/5 -Warmup.vtt 16.8 kB
  • 5 - Dynamic Programming/subtitles/5 -Iterative Policy Evaluation in Code.fr_FR.vtt 16.8 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).de_DE.vtt 16.8 kB
  • 4 - Markov Decision Proccesses/subtitles/2 -Gridworld.es_ES.vtt 16.6 kB
  • 4 - Markov Decision Proccesses/subtitles/2 -Gridworld.it_IT.vtt 16.5 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).fr_FR.vtt 16.4 kB
  • 5 - Dynamic Programming/subtitles/5 -Iterative Policy Evaluation in Code.de_DE.vtt 16.3 kB
  • 5 - Dynamic Programming/subtitles/4 -Gridworld in Code.fr_FR.vtt 16.3 kB
  • 5 - Dynamic Programming/subtitles/4 -Gridworld in Code.de_DE.vtt 16.3 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).es_ES.vtt 16.2 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/3 -Data and Environment.fr_FR.vtt 16.1 kB
  • 4 - Markov Decision Proccesses/subtitles/2 -Gridworld.pt_BR.vtt 16.1 kB
  • 5 - Dynamic Programming/subtitles/5 -Iterative Policy Evaluation in Code.it_IT.vtt 16.1 kB
  • 5 - Dynamic Programming/subtitles/5 -Iterative Policy Evaluation in Code.es_ES.vtt 16.0 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).it_IT.vtt 16.0 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/3 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.fr_FR.vtt 15.8 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).pt_BR.vtt 15.8 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).de_DE.vtt 15.7 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/2 -How to Code by Yourself (part 2).fr_FR.vtt 15.7 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/3 -Data and Environment.de_DE.vtt 15.7 kB
  • 5 - Dynamic Programming/subtitles/4 -Gridworld in Code.es_ES.vtt 15.7 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).es_ES.vtt 15.6 kB
  • 5 - Dynamic Programming/subtitles/5 -Iterative Policy Evaluation in Code.pt_BR.vtt 15.6 kB
  • 12 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/subtitles/2 -How to Code by Yourself (part 2).de_DE.vtt 15.5 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).it_IT.vtt 15.5 kB
  • 13 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/subtitles/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).pt_BR.vtt 15.5 kB
  • 4 - Markov Decision Proccesses/2 -Gridworld.vtt 15.5 kB
  • 11 - Setting Up Your Environment (FAQ by Student Request)/subtitles/3 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.de_DE.vtt 15.4 kB
  • 2 - Return of the Multi-Armed Bandit/subtitles/19 -Thompson Sampling With Gaussian Reward Theory.fr_FR.vtt 15.4 kB
  • 5 - Dynamic Programming/subtitles/4 -Gridworld in Code.it_IT.vtt 15.3 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/3 -Data and Environment.es_ES.vtt 15.3 kB
  • 5 - Dynamic Programming/subtitles/8 -Policy Improvement.fr_FR.vtt 15.3 kB
  • 8 - Approximation Methods/subtitles/3 -Feature Engineering.fr_FR.vtt 15.2 kB
  • 5 - Dynamic Programming/subtitles/4 -Gridworld in Code.pt_BR.vtt 15.2 kB
  • 10 - Stock Trading Project with Reinforcement Learning/subtitles/3 -Data and Environment.pt_BR.vtt 15.1 kB
  • 2 - Return of the Multi-Armed Bandit/15 -Bayesian Bandits Thompson Sampling Theory (pt 1).vtt 15.1 kB
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  • 6 - Monte Carlo/subtitles/2 -Monte Carlo Policy Evaluation.fr_FR.vtt 15.0 kB
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