![[PyTorch] 쉽고 빠르게 배우는 NLPCourse Thumbnail](https://cdn.inflearn.com/public/courses/325056/course_cover/b66025dd-43f5-4a96-8627-202b9ba9e038/pytorch-nlp-eng.png?f=avif&w=420)
[PyTorch] 쉽고 빠르게 배우는 NLP
코코
기본적인 자연어처리 기법(Natural Language Processing)과 딥러닝을 활용한 다양한 텍스트 task에 대해 다룹니다.
Intermediate
딥러닝, 인공신경망, PyTorch
Reinforcement learning made easy with R
We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.
What you will learn!
Reinforcement learning theory
From Q-learning to Deep Reinforcement Learning
Several reinforcement learning techniques for exploration
🙆🏻♀ Beyond Q-learning and Deep Q-learning to RND🙆🏻♂
🗒 Course Introduction
The reinforcement learning boom started with AlphaGo. Did you know that reinforcement learning was an algorithm that existed long before AlphaGo?
Reinforcement learning is generally known as a field with a high barrier to entry. While the emergence of AlphaGo sparked interest, the complex nature of the subject makes it challenging to learn. For those who have been eager to learn reinforcement learning but have been hesitant to even begin, this course summarizes the key points. From Q-learning to DQN, and beyond DQN, this course explores the sparse reward problem, a key challenge in reinforcement learning, and various ideas for solving it. This course will provide a comprehensive overview of reinforcement learning in a short period of time.
🌈 What on earth is reinforcement learning?
We will explain step-by-step, using examples, what reinforcement learning is, what elements it contains, and how learning progresses.
🌈 Q-learning that you can solve by hand
Explaining it in words alone isn't enough. Let's try solving Q-learning problems ourselves to truly grasp the concepts of reinforcement learning.
🌈 DQN, the foundation of deep reinforcement learning
This book summarizes the core concepts of Deep Reinforcement Learning, from Deep Q-network (DQN) to various DQN variants including PerDQN, actorcritic, and Self-Imitation learning.
🌈 Sparse reward problem, the main problem of reinforcement learning
We will discuss the sparse reward problem, which is a major problem in reinforcement learning, and discuss various techniques to solve it.
We mainly talk about 'curiosity' or 'prediction error' and introduce several algorithms that utilize them.
(SIL, Random Network Distillation, etc.)
🌈 Implementing DQN/ActorCritic/SIL/RND directly in R
Unless you implement it yourself with code, you only know half of it, right? Let's write reinforcement learning algorithms for the most important models in R and examine the results together.
And let's also see if RND for Exploration really works.
🙋🏻♂️ Expected questions related to the lecture
Q. Do you have any player knowledge?
A. It would be good to have a basic understanding of machine learning and NN.
Q. Don't you do any practical training with Python?
A. Currently, I have uploaded the lecture by implementing the practical code in R, and I plan to upload the practical code in Python in the future. (I will upload it in addition to this lecture, rather than opening it as another lecture.)
Recommended for
these people
Who is this course right for?
For those who want to learn reinforcement learning easily
Anyone who wants to learn the entire reinforcement learning in a short period of time
Need to know before starting?
Intermediate R programming skills
Basic understanding of neural networks
Basic knowledge of machine learning
8,297
Learners
501
Reviews
136
Answers
4.4
Rating
20
Courses
학부에서는 통계학을 전공하고 산업공학(인공지능) 박사를 받고 여전히 공부중인 백수입니다.
수상
ㆍ 제6회 빅콘테스트 게임유저이탈 알고리즘 개발 / 엔씨소프트상(2018)
ㆍ 제5회 빅콘테스트 대출 연체자 예측 알고리즘개발 / 한국정보통신진흥협회장상(2017)
ㆍ 2016 날씨 빅데이터 콘테스트/ 기상산업 진흥원장상(2016)
ㆍ 제4회 빅콘테스트 보험사기 예측 알고리즘 개발 / 본선진출(2016)
ㆍ 제3회 빅콘테스트 야구 경기 예측 알고리즘 개발 / 미래창조과학부 장관상(2015)
* blog : https://bluediary8.tistory.com
주로 연구하는 분야는 데이터 사이언스, 강화학습, 딥러닝 입니다.
크롤링과 텍스트마이닝은 현재는 취미로 하고있습니다 :)
크롤링을 이용해서 인기있는 커뮤니티 글만 수집해서 보여주는 마롱이라는 앱을 개발하였고
전국의 맛집리스트와 블로그를 수집해서 맛집 추천 앱도 만들었었죠 :) (시원하게 말아먹..)
지금은 인공지능을 연구하는 박사과정생입니다.
Curriculum
All
20 lectures ∙ (4hr 31min)
Course Materials:
Lecture resources
Published:
Last updated:
Reviews
All
3 reviews
4.3
3 reviews
chihooi1985Reviews 5
∙
Average Rating 5.0
- jjlee
Reviews 2
∙
Average Rating 3.5
- 군부대옆공대
Reviews 5
∙
Average Rating 5.0
$42.90
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