Artificial Intelligence: Reinforcement Learning in Python
Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
Description
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.
We saw AIs playing video games like Doom and Super Mario.
Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance. if that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially. Learning about supervised and unsupervised machine learning is no small feat. To date, I have over SIXTEEN (16!) courses just on those topics alone.
And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
It’s led to new and amazing insights both in behavioural psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence. What’s covered in this course?
- The multi-armed bandit problem and the explore-exploit dilemma
- Ways to calculate means and moving averages and their relationship to stochastic gradient descent
- Markov Decision Processes (MDPs)
- Dynamic Programming
- Monte Carlo
- Temporal Difference (TD) Learning (Q-Learning and SARSA)
- Approximation Methods (i.e. how to plug in a deep neural network or another differentiable model into your RL algorithm)
- Project: Apply Q-Learning to build a stock trading bot
Who this course is for:
- Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
- Both students and professionals
Requirements
- Calculus (derivatives)
- Probability / Markov Models
- Numpy, Matplotlib
- Beneficial ave experience with at least a few supervised machine learning methods
- Gradient descent
- Good object-oriented programming skills
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Download Link
https://mega.nz/folder/VgUCHZhD#38Qqr4nyLlcCztn0PQv_Ew
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