├── README.md ├── machine_learning.md └── programming.md /README.md: -------------------------------------------------------------------------------- 1 | ## MINT Lab's Know-Where 2 | These days the MINT lab think that _know-where_ is more important than _know-how_. _[Know-how](https://en.wikipedia.org/wiki/Know-how)_ is a term for practical knowledge on how to accomplish something. We use a new term, _know-where_, to emphasize to know where your desired information is available and organized. It is usually referred as _[awesome](https://github.com/topics/awesome)_ in Github. 3 | 4 | The MINT lab would like to share our _know-where_ which contains useful hyperlinks for our researches and studies on _robotics_ and _computer vision_. It is an extension of [Sunglok Choi's old collections of useful links](https://sites.google.com/site/sunglok/). The topics are as follows: 5 | 6 | * Computing 7 | * [Programming](programming.md) 8 | * [Machine Learning and Deep Learning](machine_learning.md) 9 | * Mathematics 10 | * Robotics 11 | * Computer Vision 12 | -------------------------------------------------------------------------------- /machine_learning.md: -------------------------------------------------------------------------------- 1 | ### Machine Learning 2 | #### M1. Books, Tutorials, and References 3 | * [Machine Learning](https://www.coursera.org/learn/machine-learning?action=enroll), Andrew Ng, Coursera 4 | * [Machine Learning from Scratch](https://github.com/eriklindernoren/ML-From-Scratch), Erik Linder-Noren 5 | 6 | 7 | ### Deep Learning 8 | #### D1. Books, Tutorials, and References 9 | * **[모두를 위한 머신러닝/딥러닝 강의](https://hunkim.github.io/ml/)**, 김성훈 (in Korean) 10 | * It includes Season 1 with TensorFlow, Season RL, Season NLP. 11 | * [Season 2 with Pytorch](https://deeplearningzerotoall.github.io/season2/) is at the separate website. 12 | * **[NYU DS-GA 1008: Deep Learning](https://atcold.github.io/pytorch-Deep-Learning/)**, Yann LeCun and Alfredo Canziani (Github) 13 | * **[MIT Deep Learning and Artificial Intelligence Lectures](https://deeplearning.mit.edu/)**, Lex Fridman 14 | * [Deep Learning with PyTorch](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf), Eli Stevens, Luca Aniga, and Thomas Viehmann 15 | * [My Humble DL Tutorial with PyTorch](https://github.com/mint-lab/dl_tutorial), Sunglok Choi 16 | 17 | #### D2. PyTorch 18 | * [The PyTorch official homepage](https://pytorch.org/) 19 | * [(Local) Installation](https://pytorch.org/get-started/locally/) 20 | * cf. [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive), NVIDIA: To download a specific version of CUDA 21 | * [Tutorials](https://pytorch.org/tutorials/) ([translation in Korean](https://tutorials.pytorch.kr/)) 22 | * [API Reference](https://pytorch.org/docs/) (everything) and [Cheat Sheet](https://pytorch.org/tutorials/beginner/ptcheat.html) (brief summary) 23 | 24 | #### D3. Online Demos 25 | * [ConvNetJS](https://cs.stanford.edu/people/karpathy/convnetjs/), Andrej Karpathy 26 | * [TensorFlow Playground](http://playground.tensorflow.org/) 27 | 28 | 29 | #### Special Topic on Computer Vision 30 | * [Stanford CS231n: CNN for Visual Recognition](http://cs231n.stanford.edu/), Fei-Fei Li and many others 31 | * Useful tools: [OpenCV](https://opencv.org/) 32 | 33 | 34 | ### Special Topic on Natural Language Processing 35 | #### N1. Books, Tutorials, and References 36 | * [Stanford CS224n: NLP with DL](http://web.stanford.edu/class/cs224n/) 37 | * **[딥러닝을 이용한 자연어처리 입문](https://wikidocs.net/book/2155)**, 유원준, Wikidocs (in Korean) 38 | * His series of online books: [딥러닝을 이용한 자연어처리 심화](https://wikidocs.net/book/2159), **[PyTorch로 시작하는 딥러닝 입문](https://wikidocs.net/book/2788)** 39 | * **[딥러닝을 이용한 자연어처리](https://www.edwith.org/ai331/)**, [조경현](https://kyunghyuncho.me/), [edwith](https://www.edwith.org/) (in Korean) 40 | * His [course slides](https://drive.google.com/file/d/1JUpXPchZVXe0wkAiYLKf8ySXcGRpnU7M/view?usp=sharing) are available at his homepage. 41 | * **[NL Tutorial](https://github.com/graykode/nlp-tutorial)** and [NL Study Roadmap](https://github.com/graykode/nlp-roadmap), [Tae Hwan Jung](https://graykode.github.io/) 42 | * [BGU 202-2-5381: Natural Language Processing](https://www.cs.bgu.ac.il/~elhadad/nlp21.html), Michael Elhadad 43 | 44 | #### N2. Blogs and articles 45 | * [Ki's blog](https://kh-kim.github.io/), 김기현 (in Korean) 46 | 47 | #### N3. Korean Language 48 | * [한국어 임베딩](https://ratsgo.github.io/embedding/), 이기창 49 | * Useful collections of links 50 | * [한국어 NLP 관련 링크 정리](https://github.com/datanada/Awesome-Korean-NLP), 조재민 51 | * [한국어 자연어처리 데이터셋 목록](https://littlefoxdiary.tistory.com/42) / [한국어 (사전훈련된) 언어모델 목록](https://littlefoxdiary.tistory.com/81), 아기여우의 자기계발로그 52 | * [Awesome Korean Data](https://github.com/songys/AwesomeKorean_Data), 송영숙 53 | 54 | #### N4. Tools 55 | * [NLTK](http://www.nltk.org/) ([Book](http://www.nltk.org/book/)), [spaCy](https://spacy.io/) 56 | * [Gensim](https://radimrehurek.com/gensim/) 57 | -------------------------------------------------------------------------------- /programming.md: -------------------------------------------------------------------------------- 1 | ### [Python](https://www.python.org/) 2 | #### P1. Books, Tutorials, and References 3 | * [The Python Tutorial](https://docs.python.org/3/tutorial/index.html), The Python official homepage 4 | * [The Python Standard Library](https://docs.python.org/3/library/index.html), The Python official homepage 5 | * **[Real Python Tutorials](https://realpython.com/)**, Real Python 6 | * [The Hitchhiker’s Guide to Python](https://docs.python-guide.org/), Kenneth Reitz and Tanya Schlusser ([Korean translation](https://python-guide-kr.readthedocs.io/ko/latest/)) 7 | * [Code Style](https://docs.python-guide.org/writing/style/) (Bad and Good) 8 | * [점프 투 파이썬](https://wikidocs.net/book/1), 박응용, Wikidocs (in Korean) 9 | * [예제로 배우는 파이썬 프로그래밍](http://pythonstudy.xyz/), Alex Lee (in Korean) 10 | 11 | #### P2. [NumPy](https://numpy.org/), [SciPy](https://www.scipy.org/), and [Matplotlib](https://matplotlib.org/) 12 | * [SciPy](https://www.scipy.org/): A Python-based open-source computing tool (including NumPy and Matplotlib) 13 | * [SciPy Cookbook](https://scipy-cookbook.readthedocs.io/) 14 | * [SciPy Lecture Notes](http://scipy-lectures.org/) 15 | * **[Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)**, Jake VanderPlas 16 | * It covers IPython, NumPy, Pandas, Matplotlib, and scikit-learn. 17 | * **[데이터 사이언스 스쿨](https://datascienceschool.net/)** (in Korean) 18 | * It covers Python, NumPy, SymPy, SciPy, pgmpy, Pandas, Matplotlib, Seaborn, scikit-learn, NLTK, and more. 19 | 20 | #### P3. Environments 21 | * [Anaconda](https://anaconda.com/) and `conda` 22 | * Install a package: `conda install package_to_install` 23 | * cf. [Conda command reference](https://docs.conda.io/projects/conda/en/latest/commands.html) 24 | * [Update Anaconda](https://docs.anaconda.com/anaconda/install/update-version/): `conda update --all` 25 | * [Virtual environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) 26 | * Create a virtual environment: `conda create --name venv_name python` 27 | * If you want specify version, add the version number such as `python=3.6`. 28 | * If you install other packages, add their names such as `anaconda` to include all anaconda packages 29 | * List all virtual environments: `conda env list` 30 | * Activate the virtual environment: `conda activate venv_name` 31 | * List all packages installed in the current virtual environment: `conda list` 32 | * Deactivate the current virtual environment: `conda deactivate` 33 | * Remove the virtual environment: `conda env remove --name venv_name` 34 | * [Jupyter](https://jupyter.org/) Notebook 35 | * [Online viewer and convertor](https://htmtopdf.herokuapp.com/ipynbviewer/) 36 | * [Google Colaboratory](https://colab.research.google.com/) (in short _Colab_) 37 | 38 | ### C/C++ 39 | #### C1. Books, Tutorials, and References 40 | * [cplusplus.com](http://www.cplusplus.com/): C and C++ reference and resources 41 | * [C++ Programming](http://en.wikibooks.org/wiki/C%2B%2B_Programming), Wikibooks 42 | * [C++ 이야기](https://wikidocs.net/book/2380) (in Korean), SEADOG, Wikidocs 43 | 44 | ### Others 45 | * [W3 Schools](https://www.w3schools.com/) 46 | * It covers almost everything such as HTML, CSS, XML, ..., Python, Java, C/C++, ..., SQL, PHP, ... 47 | * [Free Compilers and Interpreters](http://www.thefreecountry.com/compilers/index.shtml) 48 | * [Cheat-Sheet.org](http://www.cheat-sheets.org/): All technical reference for one page 49 | --------------------------------------------------------------------------------