The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks
What you’ll learn
Advanced AI: Deep Reinforcement Learning in Python Course Site
- Build various deep learning agents (including DQN and A3C)
- Apply a variety of advanced reinforcement learning algorithms to any problem
- Q-Learning with Deep Neural Networks
- Policy Gradient Methods with Neural Networks
- Reinforcement Learning with RBF Networks
- Use Convolutional Neural Networks with Deep Q-Learning
- Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
- College-level math is helpful
- Experience building machine learning models in Python and Numpy
- Know how to build ANNs and CNNs using Theano or Tensorflow
This course is all about the application of deep learning and neural networks to reinforcement learning.
Reinforcement learning has been around since the 70s but none of this has been possible until now.
The world is changing at a very fast pace.
We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.
Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.
This is such a fascinating perspective, it can even make supervised/unsupervised machine learning and “data science” seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world?
While deep reinforcement learning and AI has a lot of potentials, it also carries with it a huge risk.
Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk.
One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we’ll be making heavy use of in this course.
It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments.
In this course, we’ll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:
- Mountain Car
- Atari games
To train effective learning agents, we’ll need new techniques.
Thanks for reading, and I’ll see you in class!
- College-level math is helpful (calculus, probability)
- Object-oriented programming
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations
- Linear regression
- Gradient descent
- Know how to build ANNs and CNNs in Theano or TensorFlow
- Markov Decision Processes (MDPs)
- Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs
TIPS (for getting through the course):
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
- Professionals and students with strong technical backgrounds who wish to learn state-of-the-art AI techniques
- Last updated 12/2019
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Content From: https://www.udemy.com/course/deep-reinforcement-learning-in-python/