Reinforcement learning core theory that is easy to understand

Name: Reinforcement learning core theory that is easy to understand
Price: 38500 KRW
Rating: 3.5 (2 reviews)

In this lecture, you will learn the basic theory of reinforcement learning.

(3.5) 2 reviews

79 learners

Chris Song

Reinforcement Learning(RL)

Tensorflow

Machine Learning(ML)

What you will gain after the course

Reinforcement Learning Basics Theory
Tensorflow 2.0 Reinforcement Learning Programming

Why You Should Learn Reinforcement Learning Now 😆

Simply put, reinforcement learning refers to a computer program learning what actions to take in a given situation . It can be understood as a type of machine learning that can easily solve difficult decision-making problems. Reinforcement learning is one of the most essential aspects of AI decision-making, enabling machines to design programs that interact with each other and reach optimal conclusions.

But it's hard. It's hard. It's too hard 😱

Despite our desire to study reinforcement learning, studying it alone is incredibly difficult. Here are two major reasons:

It is difficult to understand the principles contained in representative reinforcement learning formulas.
A variety of concepts are mixed in one algorithm, such as TD vs MC, Value-based vs Policy-based, etc.

Even if you have a solid foundation, your learning speed will be boosted 🔥

If you're studying reinforcement learning, you might dream of cutting-edge papers and dazzling demos, but for a more distant future, you need to thoroughly master the fundamentals of reinforcement learning. This course will provide you with a deep understanding of the fundamental concepts of reinforcement learning and help you achieve the following:

How you will look after taking this course!

You will be able to clearly understand the problem definition of reinforcement learning.
You will be able to understand MDP, dynamic programming.
You can learn about the core techniques of reinforcement learning, such as temporal difference learning and Monte Carlo methods, step by step.
If you look at the papers on reinforcement learning, the formulas begin to make sense.

What's special about my lecture 😗

Just pick out each core concept of reinforcement learning!
Multi-armed bandits, Markov decision processes, dynamic programming, Monte Carlo methods, etc.
We will teach you the core concepts necessary for reinforcement learning from the basics.
Understand which reinforcement learning techniques to use for a specific problem!
It doesn't end with just knowing the theory. You will experience the actual practice process with me.
You will be able to intuitively understand how to respond in certain situations.
Embody concepts through various practices!
After learning the basic theory, you can immediately apply what you have learned through practice.
Let me help you make it yours.

What you will learn in this lecture 😝

Reinforcement Learning Basics

Learn about the basic theory and framework of reinforcement learning, and explore basic concepts related to AI reward systems, such as multi-armed bandits, Markov states, rewards, and state transitions.

dynamic programming

Based on the Markov decision process we learned earlier, we will learn about the definition and application of dynamic programming.

Monte Carlo method

Learn the basic concepts of the Monte Carlo method, which is used when the values you want to calculate are complex.

Time-lapse learning

We'll explore the fundamental concepts of temporal learning, a form of learning directly from real-world learning experiences, compare it directly with the Monte Carlo method, and see in what situations it can be utilized.

Model-based reinforcement learning

Learn about the concept of models in machine learning, explore reinforcement learning theory based on tables and models, and practice it yourself.

Policy-based reinforcement learning

Learn about policy-based reinforcement learning, which selects actions based on state rather than determining actions based on a value function.

I am this kind of person 😝

My career is as follows:

Current) Riiid VP of AIOps
Current) Google Developer Expert for ML
Former Naver AI Research Engineer
Former Kakao Data Engineer

Before attending the lecture, please check any questions you may have in advance! 😝

Q. Is this a course that non-majors and beginners can also take?

Yes, that's right. Since we're covering fundamental concepts, I'll explain them step-by-step so even non-specialists can understand them clearly.

Q. Why should I learn reinforcement learning?

I believe the future of artificial intelligence lies in reinforcement learning. I'm so convinced of its importance that I've chosen AI Production and reinforcement learning as two key words in my career.

Q. What are the benefits of learning reinforcement learning?

This will allow us to build a theoretical foundation for how artificial intelligence makes decisions in given situations.

Q. Is there anything I need to prepare before attending the lecture?

It will be helpful to have some basic knowledge of Python to take the course.

Q. What level of content is covered in the class?

We will cover basic theory and simple practical exercises.

By any chance, have you taken this course? ✨

Machine Learning Lecture by Chris Song, Knowledge Sharer

1. Machine Learning Engineer Practice

Recommended for
these people

Who is this course right for?

For those who want to learn the basics of reinforcement learning
For those who want to understand the principles of AlphaGo
Those planning to go on to graduate school in machine learning
For those who want to change their career to machine learning

Need to know before starting?

Python Basics

Hello
This is

1,056

Learners

Reviews

Answers

4.4

Rating

Courses

(현) 뤼이드 VP of AIOps

(현) Google Developer Expert for Machine Learning

(전) Naver - AI Research Engineer

(전) Kakao - Data Engineer

Curriculum

All

12 lectures ∙ (5hr 2min)

Section 1. Reinforcement Learning Basics

3 lectures ∙ (1hr 12min)

1. Introduction to Reinforcement Learning
21:28
2. Multi-armed bandit
18:57
3. Markov Decision Process
32:33

Section 2. Dynamic Programming

2 lectures ∙ (39min)

4. Dynamic Programming
12:53
5. Dynamic Programming Practice
26:58

Section 3. Monte Carlo method

2 lectures ∙ (54min)

6. Monte Carlo method
26:32
7. Monte Carlo Method Practice
27:49

Section 4. Time-lag learning

2 lectures ∙ (53min)

Section 5. Model-based Reinforcement Learning (Model-based & Model-free RL)

2 lectures ∙ (44min)

Section 6. Policy-based Reinforcement Learning

1 lectures ∙ (37min)

Published:

Last updated:

Reviews

All

2 reviews

3.5

2 reviews

seonwhee6774
Reviews 4
∙
Average Rating 3.8
4
100% enrolled
The curriculum seems to be just right for beginners. There aren't too many or too few courses, and I hope there will be an intermediate level course to follow this course.
restful3
Reviews 21
∙
Average Rating 4.4
3
100% enrolled
The lecture itself was okay... but I don't understand why the faces were placed on the left. It covers the bottom of the slides, making it difficult to see.

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Reinforcement learning core theory that is easy to understand

What you will gain after the course

Why You Should Learn Reinforcement Learning Now 😆

But it's hard. It's hard. It's too hard 😱

Even if you have a solid foundation, your learning speed will be boosted 🔥

What's special about my lecture 😗

What you will learn in this lecture 😝

Reinforcement Learning Basics

dynamic programming

Monte Carlo method

Time-lapse learning

Model-based reinforcement learning

Policy-based reinforcement learning

I am this kind of person 😝

My career is as follows:

Before attending the lecture, please check any questions you may have in advance! 😝

By any chance, have you taken this course? ✨

Machine Learning Lecture by Chris Song, Knowledge Sharer

Recommended for these people

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Reviews

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