Welcome to the world of AI, machine learning, deep learning, and robotics.
Before you ride our roller coaster (M2177.0058: Basic mathematics and programming practice for machine learning, M2866.0037: Autonomous Robot Intelligence, M2866.004300: Artificial Intelligence for Manufacturing), we recommend you to watch and read the following items during summer or winter vacation, which can help to terminate assignments and final project of the courses. [More]
Online lecture for programming language (Python)
[입문] 한입에 쏙 파이썬 | Python 강좌 | 김왼손의 왼손코딩
파이썬 기초(Python for Beginners)
6.0001 Introduction to Computer Science and Programming in Python. Fall 2016
파이썬(Python) 배우기 - 초심자를 위한 기초강의모음
Python programming environment
Google Colab
Anaconda installation for Jupyter notebook
Online lectures for Machine learning or Deep learning
Machine Learning — Andrew Ng, Stanford University
CS229 Machine Learning — Andrew Ng, (Autumn 2018) Stanford University
Neural Networks for Machine Learning — Geoffrey Hinton, Univ. of Toronto
CS231n Convolutional Neural Networks for Visual Recognition (Spring 2017) — Stanford
CS224n Natural Language Processing with Deep Learning (Winter 2019) — Stanford
Introduction to reinforcement learning — David Silver, University College London.
CS234: Reinforcement Learning (Winter 2019) — Stanford
CS294-112 Deep Reinforcement Learning Fall 2017 — UC Berkeley
MIT 6.S191: Introduction to Deep Learning, (2019-2021)
Books for beginners
타리크 라시드, "신경망 첫걸음, 수포자도 이해하는 신경망 동작 원리와 딥러닝 기초", 한빛미디어
사이토 고키, "밑바닥부터 시작하는 딥러닝", 한빛미디어
김진중, "골빈해커의 3분 딥러닝 텐서플로 코드로 맛보는 CNN, AE, GAN, RNN, DQN (+ Inception)", 한빛미디어
아서 줄리아니, "강화학습 첫걸음 텐서플로로 살펴보는 Q 러닝, MDP, DQN, A3C 강화학습 알고리즘," 한빛미디어
아라키 마사히로, 만화로 쉽게 배우는 머신러닝, 성안당
Basic mathematics to understand Machine learning or Deep learning
Essence of linear algebra — 3Blue1Brown
Neural networks — 3Blue1Brown