Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
What you'll learn
·
Apply gradient-based supervised machine learning methods to
reinforcement learning
·
Understand reinforcement learning on a technical level
·
Understand the relationship between reinforcement learning and
psychology
·
Implement 17 different reinforcement learning algorithms
Requirements
·
Calculus (derivatives)
·
Probability / Markov Models
·
Numpy, Matplotlib
·
Beneficial to have experience with at least a few supervised
machine learning methods
·
Gradient descent
·
Good object-oriented programming skills
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.
Reinforcement learning has recently become popular for doing
all of that and more.
Much like deep learning, a lot of
the theory was discovered in the 70s and 80s but it hasn’t been until recently
that we’ve been able to observe first hand the amazing results that are
possible.
In 2016 we saw Google’s AlphaGo beat
the world Champion in Go.
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
behavioral 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 other differentiable model into your
RL algorithm)
- Project: Apply Q-Learning to
build a stock trading bot
If you’re ready to take on a brand new
challenge, and learn about AI techniques that you’ve never seen before in
traditional supervised machine learning, unsupervised machine learning, or even
deep learning, then this course is for you.
See you in class!
"If you can't implement it, you don't
understand it"
- Or as the great physicist
Richard Feynman said: "What I cannot create, I do not
understand".
- My courses are the ONLY courses
where you will learn how to implement machine learning algorithms from
scratch
- Other courses will teach you
how to plug in your data into a library, but do you really need help with
3 lines of code?
- After doing the same thing with
10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and
just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
- Calculus
- Probability
- Object-oriented programming
- Python coding: if/else, loops,
lists, dicts, sets
- Numpy coding: matrix and vector
operations
- Linear regression
- Gradient descent
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture
"Machine Learning and AI Prerequisite Roadmap" (available
in the FAQ of any of my courses, including the free Numpy course)
Who this course is
for:
·
Anyone who wants to learn about artificial intelligence, data
science, machine learning, and deep learning
·
Both students and professionals
https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/
What you'll learn
·
Apply gradient-based supervised machine learning methods to
reinforcement learning
·
Understand reinforcement learning on a technical level
·
Understand the relationship between reinforcement learning and
psychology
·
Implement 17 different reinforcement learning algorithms
·
Calculus (derivatives)
·
Probability / Markov Models
·
Numpy, Matplotlib
·
Beneficial to have experience with at least a few supervised
machine learning methods
·
Gradient descent
·
Good object-oriented programming skills
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.
Reinforcement learning has recently become popular for doing
all of that and more.
Much like deep learning, a lot of
the theory was discovered in the 70s and 80s but it hasn’t been until recently
that we’ve been able to observe first hand the amazing results that are
possible.
In 2016 we saw Google’s AlphaGo beat
the world Champion in Go.
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
behavioral 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 other differentiable model into your
RL algorithm)
- Project: Apply Q-Learning to
build a stock trading bot
If you’re ready to take on a brand new
challenge, and learn about AI techniques that you’ve never seen before in
traditional supervised machine learning, unsupervised machine learning, or even
deep learning, then this course is for you.
See you in class!
"If you can't implement it, you don't
understand it"
- Or as the great physicist
Richard Feynman said: "What I cannot create, I do not
understand".
- My courses are the ONLY courses
where you will learn how to implement machine learning algorithms from
scratch
- Other courses will teach you
how to plug in your data into a library, but do you really need help with
3 lines of code?
- After doing the same thing with
10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and
just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
- Calculus
- Probability
- Object-oriented programming
- Python coding: if/else, loops,
lists, dicts, sets
- Numpy coding: matrix and vector
operations
- Linear regression
- Gradient descent
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture
"Machine Learning and AI Prerequisite Roadmap" (available
in the FAQ of any of my courses, including the free Numpy course)
Who this course is
for:
·
Anyone who wants to learn about artificial intelligence, data
science, machine learning, and deep learning
·
Both students and professionals
https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/
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