Advanced AI: Deep Reinforcement Learning in Python
Advanced AI: Deep Reinforcement Learning in Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

Created by Lazy Programmer Team, Lazy Programmer Inc.
Language English

Advanced AI: Deep Reinforcement Learning in Python

Advanced AI: Deep Reinforcement Learning in Python
Advanced AI: Deep Reinforcement Learning in Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

Created by Lazy Programmer Team, Lazy Programmer Inc.
Language English
1. Introduction and Logistics
  • 1. Introduction and Outline 00:07:23
  • 2. Where to get the Code 00:05:01
  • 3. How to Succeed in this Course 00:05:18
  • 4. Tensorflow or Theano - Your Choice! 00:04:09
2. The Basics of Reinforcement Learning
  • 1. Reinforcement Learning Section Introduction 00:06:34
  • 2. Elements of a Reinforcement Learning Problem 00:20:18
  • 3. States Actions Rewards Policies 00:09:24
  • 4. Markov Decision Processes (MDPs) 00:10:07
  • 5. The Return 00:04:56
  • 6. Value Functions and the Bellman Equation 00:09:53
  • 7. What does it mean to “learn” 00:07:18
  • 8. Solving the Bellman Equation with Reinforcement Learning (pt 1) 00:09:48
  • 9. Solving the Bellman Equation with Reinforcement Learning (pt 2) 00:12:01
  • 10. Epsilon-Greedy 00:06:09
  • 11. Q-Learning 00:14:15
  • 12. How to Learn Reinforcement Learning 00:05:56
  • 13. Suggestion Box 00:03:03
3. OpenAI Gym and Basic Reinforcement Learning Techniques
  • 1. OpenAI Gym Tutorial 00:05:43
  • 2. Random Search 00:05:48
  • 3. Saving a Video 00:02:18
  • 4. CartPole with Bins (Theory) 00:03:51
  • 5. CartPole with Bins (Code) 00:06:25
  • 6. RBF Neural Networks 00:10:27
  • 7. RBF Networks with Mountain Car (Code) 00:05:28
  • 8. RBF Networks with CartPole (Theory) 00:01:55
  • 9. RBF Networks with CartPole (Code) 00:03:11
  • 10. Theano Warmup 00:03:04
  • 11. Tensorflow Warmup 00:02:25
  • 12. Plugging in a Neural Network 00:03:40
  • 13. OpenAI Gym Section Summary 00:03:28
4. TD Lambda
  • 1. N-Step Methods 00:03:14
  • 2. N-Step in Code 00:03:40
  • 3. TD Lambda 00:07:36
  • 4. TD Lambda in Code 00:03:00
  • 5. TD Lambda Summary 00:02:21
5. Policy Gradients
  • 1. Policy Gradient Methods 00:11:38
  • 2. Policy Gradient in TensorFlow for CartPole 00:07:19
  • 3. Policy Gradient in Theano for CartPole 00:04:14
  • 4. Continuous Action Spaces 00:04:16
  • 5. Mountain Car Continuous Specifics 00:04:12
  • 6. Mountain Car Continuous Theano 00:07:31
  • 7. Mountain Car Continuous Tensorflow 00:08:08
  • 8. Mountain Car Continuous Tensorflow (v2) 00:06:11
  • 9. Mountain Car Continuous Theano (v2) 00:07:31
  • 10. Policy Gradient Section Summary 00:01:36
6. Deep Q-Learning
  • 1. Deep Q-Learning Intro 00:03:52
  • 2. Deep Q-Learning Techniques 00:09:13
  • 3. Deep Q-Learning in Tensorflow for CartPole 00:05:09
  • 4. Deep Q-Learning in Theano for CartPole 00:04:48
  • 5. Additional Implementation Details for Atari 00:05:36
  • 6. Pseudocode and Replay Memory 00:06:15
  • 7. Deep Q-Learning in Tensorflow for Breakout 00:23:47
  • 8. Deep Q-Learning in Theano for Breakout 00:23:54
  • 9. Partially Observable MDPs 00:04:53
  • 10. Deep Q-Learning Section Summary 00:04:45
7. A3C
  • 1. A3C - Theory and Outline 00:16:30
  • 2. A3C - Code pt 1 (Warmup) 00:06:28
  • 3. A3C - Code pt 2 00:06:27
  • 4. A3C - Code pt 3 00:07:35
  • 5. A3C - Code pt 4 00:18:02
  • 6. A3C - Section Summary 00:02:05
  • 7. Course Summary 00:04:57
8. Theano and Tensorflow Basics Review
  • 1. (Review) Theano Basics 00:07:47
  • 2. (Review) Theano Neural Network in Code 00:09:17
  • 3. (Review) Tensorflow Basics 00:07:27
  • 4. (Review) Tensorflow Neural Network in Code 00:09:43
9. Setting Up Your Environment
  • 1. Windows-Focused Environment Setup 2018 00:20:20
  • 2. How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow 00:17:32
10. Extra Help With Python Coding for Beginners
  • 1. How to Code by Yourself (part 1) 00:15:54
  • 2. How to Code by Yourself (part 2) 00:09:23
  • 3. Proof that using Jupyter Notebook is the same as not using it 00:12:29
  • 4. Python 2 vs Python 3 00:04:38
  • 5. Is Theano Dead 00:10:03
11. Effective Learning Strategies for Machine Learning
  • 1. How to Succeed in this Course (Long Version) 00:10:24
  • 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:22:04
  • 3. What order should I take your courses in (part 1) 00:11:19
  • 4. What order should I take your courses in (part 2) 00:16:07
12. Appendix FAQ
  • 1. What is the Appendix 00:02:48
  • 2. BONUS Where to get Udemy coupons and FREE deep learning material 00:05:31