## (Contributors)

Teddy Lee

HongJaeKwon

Seungwoo Han

Tae Heon Kim

Steve Kwon

SW Song

K1A2

Wooil Jeong

, Pull Request !
## (Knowledge Sharings) , . - [ ](https://www.youtube.com/channel/UCt2wAAXgm87ACiQnDHQEW6Q) - [](https://teddylee777.github.io/) **** This repository is intended for personal study in machine-learning ** ** . Lecture Blog . , . ----- ## , (Video Lectures) Video . , . ** (Python), (Pandas, Numpy), (Matplotlib, Seaborn, Bokeh, Folium)** * [ (Python) - ](https://www.youtube.com/watch?v=dpwTOQri42s) * [ - ()](https://learnaday.kr/open-course/geNpyx) * (3). . * [ Python | ](https://www.youtube.com/watch?v=c2mpe9Xcp0I&list=PLGPF8gvWLYyrkF85itdBHaOLSVbtdzBww&index=1) * [ - ](https://learnaday.kr/open-course/ZiYShf) * [NumPy() - T](https://www.youtube.com/watch?v=zNrDbG4tNGo&list=PL9mhQYIlKEhf04ToiDFvNzKL0OP4W27TW) * [ (Pandas) - ](https://www.udemy.com/course/pandas-i/) * [(Pandas) () - ](https://wikidocs.net/book/4639) * [Pandas - T](https://www.youtube.com/watch?v=M_lKmt-wSvY&list=PL9mhQYIlKEhfG_gWF-DclKs6vXS6SkmQN) * [Pandas - T](https://www.youtube.com/watch?v=oNLaw2Q8Irw&list=PL9mhQYIlKEhd60Qq4r2yC7xYKIhs97FfC) * [ - ](https://www.youtube.com/watch?v=BvJhYPQSDLI&list=PLnIaYcDMsScyhT18mwY71rV_aHdP-OhLd) * [ - ](https://www.youtube.com/watch?v=TIjsrH_THhs&list=PLnIaYcDMsScyrZZXH6LTXMrOLXJ-7hznD) ** (Mathmatics) & (Statistics)** * [ - 3Blue1Brown ](https://youtu.be/ic_hG2M2nG0?feature=shared) * [ ? | - 3Blue1Brown ](https://youtu.be/ArgTeYVuJUo?feature=shared) * [ - 3Blue1Brown](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) * [Mathematical Monk Youtube()](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) * . * [ - ](https://www.youtube.com/watch?v=4xJOapwJFkg&list=PLi40YkwlJ5DnK4DTM4Fen6oZWiEBtFQe0) * [ - Chanwoo Timothy Lee ](https://www.youtube.com/watch?v=E6Dqu4THRu8&list=PLR4XxpTBVXGhnPS8zauclk12WyXotQktG) * ** (Machine Learning) & (Deep Learning)** * [Best of ML Python](https://github.com/ml-tooling/best-of-ml-python) * 840 ML ! ! * [Machine Learning with Python](https://github.com/tirthajyoti/Machine-Learning-with-Python) * Jupyter Notebook GitHub! * [Scikit Learn ](https://inria.github.io/scikit-learn-mooc/index.html) * (Scikit Learn) * (freeCodeCamp.org): https://www.youtube.com/watch?v=pqNCD_5r0IU * [Machine Learning by coursera - Andrew Ng](https://www.coursera.org/learn/machine-learning) * **** . Andrew Ng . * [ - (TEAMLAB)](https://www.youtube.com/watch?v=1Z-lT4ooSFY&list=PLBHVuYlKEkUKnfbWvRCrwSuSeYh_QUlRl) * "[ ](https://www.youtube.com/watch?v=t84jQTwMFuE&list=PLBHVuYlKEkUJcXrgVu-bFx-One095BJ8I)" . **** (3 3), * [ 1 (Tensorflow) - ](https://www.youtube.com/watch?v=BS6O0zOGX4E&index=1&list=PLlMkM4tgfjnLSOjrEJN31gZATbcj_MpUm) * . tensorflow . * [ , , - ](https://www.youtube.com/watch?v=-JWv0ed9R5g&list=PLsS-TVNjbU7clDOjpAZKud3uG8APHDq_M) * . . * [ - Idea Factory KAIST](https://www.youtube.com/watch?v=hPXeVHdIdmw&list=PLSAJwo7mw8jn8iaXwT4MqLbZnS-LJwnBd) * . * [CS231n () - Stanford](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk) * . . * [CS329S: Machine Learning Systems Design (Winter 2021)](https://stanford-cs329s.github.io/syllabus.html?fbclid=IwAR0m-M5Q4rgQIgGuQnZv_syF0sBS-A6juHc0WLN5URNBRkMJiTiDda2t_e8) * CS 329S . . * [ ()](https://www.youtube.com/channel/UCzz6ructab1U44QPI3HpZEQ) * [ - ](https://www.inflearn.com/course/%EB%8D%B0%EC%9D%B4%ED%84%B0-%EC%82%AC%EC%9D%B4%EC%96%B8%EC%8A%A4-kaggle) * , . * [ - ](https://www.youtube.com/watch?v=9QW7QL8fvv0&list=PLaTc2c6yEwmrtV81ehjOI0Y8Y-HR6GN78) * [Deep Learning by GOOGLE - Udacity](https://www.udacity.com/course/deep-learning--ud730) * 1 . (Assignment ) * [DEEP LEARNING, Spring 2020 - NYU CENTER FOR DATA SCIENCE](https://atcold.github.io/pytorch-Deep-Learning/) * Alfredo Canziani . , . * [ ](https://www.youtube.com/playlist?list=PL0oFI08O71gKEXITQ7OG2SCCXkrtid7Fq) * / . , ( ). * [TensorFlow2 - Shin's Lab](https://www.youtube.com/watch?v=-MIH2wNfylo&list=PLtm_YtKTtDkQJtgGSQnZzMJBRHyqANnQi) * . , . * [Pytorch Zero To All () - ]() * [ RL - ](https://www.youtube.com/playlist?list=PLlMkM4tgfjnKsCWav-Z2F-MMFRx-2gMGG) * [ - ](https://www.edwith.org/deeplearningchoi) * [PyTorch ()](https://tutorials.pytorch.kr/) - PyTorch * [ - ! by todaycode](https://youtu.be/CVrT23QVfxA) - (PyTorch) . 30 ! * [ AWS](http://www.awsboost.io/) - Zoom / . Sagemaker . ** ** * [ - ](https://www.kaggle.com/agileteam/bigdatacertificationkr) * , . # (By Subjects) - [Machine Learning Study ](#machine-learning-study--) - [ (Contributors) ](#-contributors-) - [ (Knowledge Sharings)](#-knowledge-sharings) - [ , (Video Lectures)](#----video-lectures) - [ (By Subjects)](#-by-subjects) - [ (Mathmatics)](#-mathmatics) - [ (Statistics)](#-statistics) - [ (Machine Learning)](#-machine-learning) - [ (Deep Learning)](#-deep-learning) - [ \& AutoML (Optimization \& AutoML)](#--automl-optimization--automl) - [ (Meta Learning)](#-meta-learning) - [ (Active Learning)](#-active-learning) - [ (Federated Learning)](#-federated-learning) - [ (Incremental Learning)](#-incremental-learning) - [ (Visualization)](#-visualization) - [LLM (Large Language Model)](#llm-large-language-model) - [ (LangChain)](#-langchain) - [ChatGPT](#chatgpt) - [ (Others)](#-others) - [ \& ](#--) - [ ?](#-) - [ \& ](#--) - [ \& ](#----) - [ (Blogs)](#-blogs) - [ (GitHub)](#--github) - [ (Web Sites)](#-web-sites) - [ (Wiki Docs)](#-wiki-docs) - [ (YouTube Channel)](#--youtube-channel) - [ (YouTube)](#--youtube) - [ (Data Scientist Story)](#---data-scientist-story) - [ (Facebook Groups)](#--facebook-groups) - [ (Library)](#-library) - [](#) - [ ](#-) - [ ](#-) - [](#) ## (Mathmatics) * **** - [, - ](https://www.youtube.com/watch?v=vS51prw_yfw) - [ - ](https://librewiki.net/wiki/%EC%88%98%ED%95%99_%EA%B8%B0%ED%98%B8) - [ e - ](https://www.youtube.com/watch?v=_EY8QUKWrhc) - [What is ln (Natural Logarithm) - Arnold Tutoring](https://www.youtube.com/watch?v=e7Yfub7xlDg) * **** - [ (Ordinary Derivative & Partial Derivative) | (Mathematics for AI) - ](https://www.youtube.com/watch?v=tQHw2EovIOM&list=PLRx0vPvlEmdAWjA5INMVJoqea18RQyUOk&index=4) - [/ 2 - | T](https://www.youtube.com/watch?v=JQe7S-gOElk&list=PL9mhQYIlKEhewXqJaTy_wd5emhDwW6JU6&index=3) - [ ? (hyperbolic functions) - TV( )](https://www.youtube.com/watch?v=3DvmUlAIPaw) * **** - [ ( ) : , - ](https://www.youtube.com/watch?v=EGEQutnxjDU&list=PLRx0vPvlEmdAWjA5INMVJoqea18RQyUOk&index=5) * **** - [/ 4 - | T](https://www.youtube.com/watch?v=0PhFyQyii7Q&list=PL9mhQYIlKEhewXqJaTy_wd5emhDwW6JU6&index=5) * **** - [ - Desmos](https://www.desmos.com/calculator?lang=ko) - . ## (Statistics) * ** ** * [ - EOStatistics](https://www.youtube.com/watch?v=1Kj0_2nrWLo&list=PLmljWRabIwWDCLjAMfTPigyTe-jtsLca1) * . , . * [ - ](https://www.youtube.com/watch?v=ZdvXXBLIBnw&list=PLEUKy_nwlzwHhkGKF7l3lWxqYKTjnnv5M) * , . * [ - ](https://www.youtube.com/watch?v=SCMyqKSuKeI&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG) * [ . (, , , , , , , , , ) - ](https://www.youtube.com/watch?v=CQA7cdxozHY) * **p-value** * [P-(p-value) ? - Sapientia a Dei](https://www.youtube.com/watch?v=5Xke4ao1g9E) * [P-Value - ](https://www.youtube.com/watch?v=tpow70KGTYY&list=PLpIPLT0Pf7IoTxTCi2MEQ94MZnHaxrP0j&index=4) * **** * [ ( .) - ](https://www.youtube.com/watch?v=qkEOVNUnnTw&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=28) * [ ( ) - ](https://www.youtube.com/watch?v=zcfMEcN1srY) * [ vs. , p-value - Data Scientist](https://www.youtube.com/watch?v=TEsXCUozAsE) * **** * [1(, , , ) - ](https://www.youtube.com/watch?v=tfvTTF4JidQ&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=19) * [2(, , , ) - ](https://www.youtube.com/watch?v=dk2d5--IBTQ&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=20) * [3( , , ) - ](https://www.youtube.com/watch?v=S1ztukK-PkM&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=21) * [ (Normal Distribution) - ](https://www.youtube.com/watch?v=sGTWFCq5OKM) * [ (Uniform Distribution) - ](https://www.youtube.com/watch?v=6xonZUbFSZ8) * **, ** * [ - Data Scientist](https://www.youtube.com/watch?v=8m5_UOqBTR4) * [ (, , ) - ](https://www.youtube.com/watch?v=ozC2vKZhd04&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=24) * [ ( , ) - ](https://www.youtube.com/watch?v=PoWiyZVgjBg&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=25) * [ ( ) - ](https://www.youtube.com/watch?v=E4MuAveSQb4&list=PLsri7w6p16vs-rMb1uXHfh3FiCk2WjEUG&index=26) * ** ** * [Bayes theorem - 3Blue1Brown](https://www.youtube.com/watch?v=HZGCoVF3YvM) * ** ** * [ ? . - 3Blue1Brown](https://www.youtube.com/watch?v=spUNpyF58BY) * ** ** * [[Signal processing] EMD (Empricial mode decomposition): ](https://neosla.tistory.com/34) * **AR, MA, ARMA, ARIMA** * [ ](https://yamalab.tistory.com/112) ## (Machine Learning) * ** (Gradient Descent)** * [ , | , 2 - 3Blue1Brown](https://www.youtube.com/watch?v=IHZwWFHWa-w) * [ () - ](https://www.youtube.com/watch?v=GEdLNvPIbiM) * [ - ](https://youtu.be/KgH3ZWmMxLE) * [ - ](https://www.youtube.com/watch?v=P4L3IntRwrc) * ** (Back Propagation)** * [Yes you should understand backprop](https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b) * [Stanford - CS231n - Introduction to Neural Networks](https://www.youtube.com/watch?v=d14TUNcbn1k&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=4) * [Stanford - CS231n - Backpropagation() - Kyoseok Song](https://www.youtube.com/watch?v=qtINaHvngm8) * [ - ](https://youtu.be/1Q_etC_GHHk) * [ - Chanwoo Timothy Lee](https://www.youtube.com/watch?v=fhrORKjjU7w) * [ 7 - TAcademy](https://www.youtube.com/watch?v=kHUvoNX8fsE) * ** (Loss Functions)** * [Stanford - CS231n - Loss Functions and Optimization](https://www.youtube.com/watch?v=h7iBpEHGVNc&index=3&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk) * ** (Linear Regression)** * [ - ](https://www.youtube.com/watch?v=-oBmMED_5rI) * [Least Squares Estimators - jbstatistics](https://www.youtube.com/watch?v=ewnc1cXJmGA) * [ - Least Squares Criterion Part 1 - patrickJMT](https://www.youtube.com/watch?v=0T0z8d0_aY4) * [ - Least Squares Criterion Part 2 - patrickJMT](https://www.youtube.com/watch?v=1C3olrs1CUw) * [ - (30 !) - ](https://www.youtube.com/watch?v=ve6gtpZV83E) * [ - (Least Square Method) - Data Scientist](https://www.youtube.com/watch?v=F-JjAoXZxf0) * [Linear Regression() - ](https://www.youtube.com/watch?v=MwadQ74iE-k&list=PLTjDXCqLsHZcnBBYcXhg-juYX-25iRusr) * [ - ](https://www.youtube.com/watch?v=umiqnfQxlac) * [/ 5 - (Regression) | T](https://www.youtube.com/watch?v=ukGvbDYCIxc&list=PL9mhQYIlKEhewXqJaTy_wd5emhDwW6JU6&index=6) * **Norm (L1 & L2)** * [/ 6 - L1/L2 (Regulaization) | T](https://www.youtube.com/watch?v=01qqdvP0sdU&list=PL9mhQYIlKEhewXqJaTy_wd5emhDwW6JU6&index=7) * [Norm (L1, L2) - ](https://www.youtube.com/watch?v=yoD5tQ1HQRU) * **Lasso, Ridge, ElasticNet** * [ 2 - LASSO, Elastic Net - ](https://www.youtube.com/watch?v=sGTWFCq5OKM) * **Support Vector Machine (SVM)** * [SVM (1) - ](https://www.youtube.com/watch?v=qFg8cDnqYCI&list=PLpIPLT0Pf7IoTxTCi2MEQ94MZnHaxrP0j&index=9) * [SVM (2) - ](https://www.youtube.com/watch?v=ltjhyLkHMls&list=PLpIPLT0Pf7IoTxTCi2MEQ94MZnHaxrP0j&index=8) * **KNN (K-Nearest Neighbors)** * [kNN(k-Nearest Neighbors) - ](https://www.youtube.com/watch?v=CyuI2F_wJWw) * ** (Logistic Regression)** * [ 1 (, ) - ](https://www.youtube.com/watch?v=l_8XEj2_9rk) * [ 2 ( , ) - ](https://www.youtube.com/watch?v=Vh_7QttroGM) * **(Decision Tree)** * [ 1 (, ) - ](https://www.youtube.com/watch?v=xki7zQDf74I) * [ (Decision Tree) - ](https://www.youtube.com/watch?v=n0p0120Gxqk) * **** * [PCA - ](https://www.youtube.com/watch?v=DUJ2vwjRQag) * [Principal Component Analysis (PCA, ) - ](https://www.youtube.com/watch?v=FhQm2Tc8Kic) * ** (Clustering)** * [ - ](https://www.youtube.com/watch?v=8zB-_LrAraw&list=PLpIPLT0Pf7IoTxTCi2MEQ94MZnHaxrP0j) ## (Deep Learning) * **** * [ ? | 1. - 3Blue1Brown](https://www.youtube.com/watch?v=aircAruvnKk) * [ ](https://nittaku.tistory.com/269) * **Convolution Neural Networks (CNN)** * [Stanford - CS231n - Convolution Neural Networks](https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=5) * [CNN : Stride MaxPooling - ](https://youtu.be/sPf0iaOzYaY) * [ML lab11-1: TensorFlow CNN Basics - ](https://www.youtube.com/watch?v=E9Xh_fc9KnQ) * **Recurrent Neural Networks (RNN)** * [Stanford - CS231n - Recurrent Neural Networks](https://www.youtube.com/watch?v=6niqTuYFZLQ&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=10) * [Programming LSTM with Keras and TensorFlow](https://www.youtube.com/watch?v=UnclHXZszpw&t=572s) * [RNN ( - Vanilla RNN) - ](https://youtu.be/PahF2hZM6cs) * [LSTM - ](https://youtu.be/bX6GLbpw-A4) * [(CS231n ) RNN, LSTM - ](https://youtu.be/2ngo9-YCxzY) * [RNN & LSTM (pytorch) - Donghoon Note](https://dhpark1212.tistory.com/entry/RNN-LSTM-%EC%84%A4%EB%AA%85-%EB%B0%8F-%EA%B5%AC%ED%98%84pytorch) * ** (Generative Adversarial Network)** * [1 GAN - D2](https://www.youtube.com/watch?v=odpjk7_tGY0) * [GAN: Generative Adversarial Networks ( ) - ](https://www.youtube.com/watch?v=AVvlDmhHgC4) * [Basic of GAN - by Idea Factory KAIST](https://www.youtube.com/watch?v=LeMnE1TIil4) * [DC GAN - by Idea Factory KAIST](https://www.youtube.com/watch?v=JOjMk-E1CnQ&list=PLSAJwo7mw8jn8iaXwT4MqLbZnS-LJwnBd) * [DC GAN - YBIGTA](https://www.youtube.com/watch?v=7btUjE2y4NA) * [Finding connections among images using CycleGAN - naver d2](https://www.youtube.com/watch?v=Fkqf3dS9Cqw) * [/ - 016 CycleGAN - hanyoseob](https://www.youtube.com/watch?v=zAVCeF5cFNc) * ** (Reinforcement Learning)** * [ - ](https://www.youtube.com/watch?v=dZ4vw6v3LcA&feature=youtu.be) * [() - ](https://www.davidsilver.uk/teaching/) * [ (10) - ](https://www.youtube.com/watch?v=wYgyiCEkwC8&list=PLpRS2w0xWHTcTZyyX8LMmtbcMXpd3s4TU) * [ (2) - ](https://www.youtube.com/watch?v=12pXaP8KPbE&list=PLpRS2w0xWHTdpMdpzuQf-w1QmCVrE2leJ) * [ (season 1) - T](https://www.youtube.com/watch?v=NrcePTbqNb4&list=PL9mhQYIlKEhfMzkhV1gsIU8cZLeEUAbLR) * [ (policy gradient) - T](https://www.youtube.com/watch?v=irxj7ThyASk&list=PL9mhQYIlKEhc-n4vu4cWChTaNMi0mwYn4) * [ - KR](https://github.com/reinforcement-learning-kr/how_to_study_rl/wiki/%EA%B0%95%ED%99%94%ED%95%99%EC%8A%B5-%EA%B4%80%EB%A0%A8-%EB%85%B8%ED%95%98%EC%9A%B0) * [ 100 - Koki Saitoh](https://koki0702.github.io/dezero-p100/) * . . * ** (Computer Vision)** * [Awesome computer vision](https://github.com/jbhuang0604/awesome-computer-vision) * . * [OpenCV - Daehee YUN Tech Blog](https://076923.github.io/posts/Python-opencv-1/) * Python C# OpenCV . * [Object Detection( ) - Deeplearning.ai](https://www.youtube.com/watch?v=GSwYGkTfOKk&list=PL_IHmaMAvkVxdDOBRg2CbcJBq9SY7ZUvs) * [Semantic Segmentation ( ) - UNet ](https://github.com/zhixuhao/unet) * [Self-Driving Car () - source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree](https://github.com/ndrplz/self-driving-car) * [ - ](https://pseudo-lab.github.io/Tutorial-Book/chapters/object-detection/Ch1-Object-Detection.html) * ** (Natural Language Processing)** * [ - ](https://www.edwith.org/deepnlp) * [Stanford - Natural Language Processing with Deep Learning](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) * [( ) - ](https://youtu.be/mxGCEWOxfe8) * [Transformer: Attention Is All You Need ( ) - ](https://www.youtube.com/watch?v=AA621UofTUA) * [(CS231n ) Attention - ](https://youtu.be/Bmx2S1dSAV0) * [ + - ](https://youtu.be/WsQLdu2JMgI) * [Seq2Seq: Sequence to Sequence Learning with Neural Networks - ](https://www.youtube.com/watch?v=4DzKM0vgG1Y) * [ "BERT"](https://www.youtube.com/watch?v=qlxrXX5uBoU&list=PL9mhQYIlKEhcIxjmLgm9X5BUtW5jMLbZD) * [ - ](https://www.youtube.com/playlist?list=PLgD4RfwkG2A5fNsi7PyhWCiIz5zU2Q6Z0) * , (Word2Vec, TF-IDF), BERT, GPT * [ - Ready-To-Use Tech](https://www.youtube.com/watch?v=Z201jwWo-xs&list=PLrLEKGJAgXxL-R9IqDH7HANWXRsS900tF) * kiyoungkim1 * ** (Speech Recognition)** * [ - T](https://www.youtube.com/watch?v=YiW7aOTZFQQ&list=PL9mhQYIlKEhdrYpsGk8X4qj3tQUuaDhrl) * **** * [Improving Deep Neural Networks: Hyperparameter Tuning](https://www.youtube.com/watch?v=1waHlpKiNyY&list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc&index=1) - Andrew Ng DNN . . * [Why Does Batch Norm Work? (Batch Norm ) - Andrew Ng](https://www.youtube.com/watch?v=nUUqwaxLnWs) * [Adam Optimization Algorithm - Andrew Ng](https://www.youtube.com/watch?v=JXQT_vxqwIs) ## & AutoML (Optimization & AutoML) * ** ** * [ feat., TSP](https://www.youtube.com/watch?v=H8beAqbiWZw) * ** ** * [[ML] (Bayesian Optimization)](https://wooono.tistory.com/102) * ** ** * [Hyperband ](https://pod3275.github.io/paper/2019/05/23/Hyperband.html) * **Neural Architecture Search** * [NASnet ](https://www.secmem.org/blog/2019/07/19/Network-Architecture-Search/) * [ENAS ](https://jayhey.github.io/deep%20learning/2018/03/15/ENAS/) * [PNAS ](https://m.blog.naver.com/PostView.nhn?blogId=za_bc&logNo=221576139392&proxyReferer=https:%2F%2Fwww.google.com%2F) ## (Meta Learning) * **** * [Meta-Learning: Learning to Learn Fast ](https://talkingaboutme.tistory.com/entry/DL-Meta-Learning-Learning-to-Learn-Fast) * ** ** * [Meta Reinforcement Learning ](https://talkingaboutme.tistory.com/entry/RL-Meta-Reinforcement-Learning) ## (Active Learning) * **** * [Active Learning - ](https://kmhana.tistory.com/4) ## (Federated Learning) * **** * [ (Federated Learning), ](https://medium.com/curg/%EC%97%B0%ED%95%A9-%ED%95%99%EC%8A%B5-federated-learning-%EA%B7%B8%EB%A6%AC%EA%B3%A0-%EC%B1%8C%EB%A6%B0%EC%A7%80-b5c481bd94b7) ## (Incremental Learning) * **** * [Incremental / Continual learning (, , )](https://ffighting.tistory.com/112) ## (Visualization) * **Bokeh** * [ Bokeh - ](https://www.youtube.com/watch?v=XbfQNJrIXZc) ## LLM (Large Language Model) * **AutoGPT** * [AutoGPT - ](https://teddylee777.github.io/machine-learning/autogpt/) * (Goal) GPT. * **FineTuning** * [KoChatGPT-replica(RLHF) ](https://github.com/airobotlab/KoChatGPT) * ChatGPT-replica . GPT fine-tuning, (PPO), RLHF, ChatGPT . Colab . * [KoAlphaca: Korean Alpaca Model based on Stanford Alpaca (feat. LLAMA and Polyglot-ko)](https://github.com/Beomi/KoAlpaca) * Stanford Alpaca , Alpaca . Lora Peft , . ## (LangChain) * ** ()** * [(langchain) OpenAI GPT (ChatOpenAI) ](https://teddylee777.github.io/langchain/langchain-tutorial-01/) * [(langchain) + (HuggingFace) ](https://teddylee777.github.io/langchain/langchain-tutorial-02/) * [(langchain) + (chat) - ConversationChain, ](https://teddylee777.github.io/langchain/langchain-tutorial-03/) * [(langchain) + (CSV, Excel) - ChatGPT ](https://teddylee777.github.io/langchain/langchain-tutorial-04/) * [(langchain) + - ](https://teddylee777.github.io/langchain/langchain-tutorial-05/) * [(langchain) + - ](https://teddylee777.github.io/langchain/langchain-tutorial-06/) * [(langchain) + PDF , Map-Reduce](https://teddylee777.github.io/langchain/langchain-tutorial-07/) * [(langchain) + PDF (Question-Answering)](https://teddylee777.github.io/langchain/langchain-tutorial-08/) * ** ** * [ Featured YouTube ](https://python.langchain.com/docs/additional_resources/tutorials) * , . . ## ChatGPT **OpenAI** * [OpenAI API Reference](https://platform.openai.com/docs/api-reference) * OpenAI API * [OpenAI Cookbook](https://cookbook.openai.com/) * OpenAI Python API . . **** * [ AI ](https://wikidocs.net/book/9451) - * AI * [ AI ](https://wikidocs.net/book/12852) - * , AI / ## (Others) * **** * [ - mecari](https://mercari.github.io/ml-system-design-pattern/README_ko.html) * **Azure ** * [Azure - ](https://www.youtube.com/watch?v=MIBPJV8krXM&list=PLSlDi2AkDv83W0Js_cjxlIg-CGKNi4VUX) * **** * [RDBMS SQL - T](https://www.youtube.com/watch?v=DeaJVvdIBFg&list=PL9mhQYIlKEheGuumYb91mCiRRpOFjErZd) * ** Prophet** * [ Prophet ! ( ) - ](https://www.youtube.com/watch?v=Sm-YBPUe3qU) * [ #1: Prophet, - ](https://www.youtube.com/watch?v=teD60NOLQL0) * [ #2: Prophet, Saturating Forecasts - ](https://www.youtube.com/watch?v=BcmyGFNl3GA) * [ #3: Prophet, Trend Change points - ](https://www.youtube.com/watch?v=LPd2WRJFxjU) ## & ### ? **Hello Kaggle!** * [Hello Kaggle! - stevekwon211 ](https://github.com/stevekwon211/Hello-Kaggle-KOR) * , , , , API * [ 4 ](https://www.youtube.com/watch?v=tu6b3xbTj6M) * with * [ 24 - Upstage](https://www.youtube.com/watch?v=TwF2EB9UCsI) * **Kaggle Tutorial | PyTorch Basic** * [Pytorch Tutorial for Deep Learning Lovers ,DATAI](https://www.kaggle.com/kanncaa1/pytorch-tutorial-for-deep-learning-lovers) * (Tensor ) , , ANN, CNN * [Conditional Generative Adversarial Network ,Arpan Dhatt](https://www.kaggle.com/arpandhatt/conditional-generative-adversarial-network) * CGAN(Conditional GAN) MNIST * [Pytorch Animal Face Classification - CNNs, Mehmet -lauda- Tekman](https://www.kaggle.com/mehmetlaudatekman/pytorch-animal-face-classification-cnns) * AFHQ( ) * [Overview of Basic GAN Architecture - Seungwon Song](https://www.kaggle.com/songseungwon/overview-of-basic-gan-architecture) * MNIST(1~9) * [Generate Fashion Images with Conditional GAN - Seungwon Song](https://www.kaggle.com/songseungwon/generate-fashion-images-with-conditional-gan) * Fashion MNIST( ) (Conditional) **Kaggle Tutorial | Image/Object Detection** * [[Train] SIIM COVID-19 Detection: FasterRCNN - Heroseo](https://www.kaggle.com/piantic/train-siim-covid-19-detection-fasterrcnn) * X-ray * [Yolo v3 Object Detection in Tensorflow - heartkilla](https://www.kaggle.com/aruchomu/yolo-v3-object-detection-in-tensorflow) * Tensorflow, Yolo v3 * [SIIM COVID-19 Detection 10+Step Tutorial (1) - Seungwon Song](https://www.kaggle.com/songseungwon/siim-covid-19-detection-10-step-tutorial-1) * Feature Engineering Image Detection **Kaggle Tutorial | Natural Language Processing** * [Beginner to Intermediate Natural Language Processing Guide - NowYSM](https://www.kaggle.com/ashishpatel26/beginner-to-intermediate-nlp-tutorial) * sklearn + logistic Regression (/ ) * [Deep Learning NLP Quora Solutions - NowYSM](https://www.kaggle.com/ashishpatel26/deep-learning-nlp-quora-solutions) * (Keras) ( , ) * [NLP Quick Start for Newbie with 9steps - Seungwon Song](https://www.kaggle.com/songseungwon/nlp-quick-start-for-newbie-with-9steps) * **Kaggle Tutorial | R Machine Learning** * [Getting staRted in R: First Steps - Rachael Tatman](https://www.kaggle.com/rtatman/getting-started-in-r-first-steps) * r * [Getting staRted in R: Load Data Into R - Rachael Tatman](https://www.kaggle.com/rtatman/getting-started-in-r-load-data-into-r) * r * [Getting staRted in R: Summarize Data - Rachael Tatman](https://www.kaggle.com/rtatman/getting-started-in-r-summarize-data) * `(%>%)` , * [Getting staRted in R: Graphing Data - Rachael Tatman](https://www.kaggle.com/rtatman/getting-started-in-r-graphing-data/) * `ggplot2` * [Welcome to Data Science in R - Rachael Tatman](https://www.kaggle.com/rtatman/welcome-to-data-science-in-r) * `modelr` , **Kaggle ** * [Winning solutions of kaggle competitions](https://www.kaggle.com/code/sudalairajkumar/winning-solutions-of-kaggle-competitions) ### & **** * [ - T](https://www.youtube.com/watch?v=9NKGaJxcrsM&list=PL9mhQYIlKEhcaivg3ltnx3DS49AAIc3qv) * , ( ) , **** * [Deep Learning Practitioner 2 - (Kakao) ](https://www.youtube.com/watch?v=zNzAAStE66o) **** * [Feature Engineering Techniques - Chris Deotte](https://www.kaggle.com/c/ieee-fraud-detection/discussion/108575) ### & ** (For Beginners)** * [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic) * . / * [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) * . (regression) * [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk/overview/evaluation) * (ROC-AUC) * [House Prices: Advanced Regression Technique](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) * ( ) ** (Vision)** * [Digit Recognizer](https://www.kaggle.com/c/digit-recognizer) * [Facial Keypoints Detection](https://www.kaggle.com/c/facial-keypoints-detection) * [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats) * [Right Whale Recognition](https://www.kaggle.com/c/noaa-right-whale-recognition) * [Intel & MobileODT Cervical Cancer Screening](https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening) ** (Time Series)** * [Web Traffic Time Series Forecasting](https://www.kaggle.com/c/web-traffic-time-series-forecasting) * [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) * [Corporacin Favorita Grocery Sales Forecasting](https://www.kaggle.com/c/favorita-grocery-sales-forecasting) * [Rossmann Store Sales](https://www.kaggle.com/c/rossmann-store-sales) **** * [TensorFlow Speech Recognition Challenge](https://www.kaggle.com/c/tensorflow-speech-recognition-challenge) ## (Blogs) * [](https://teddylee777.github.io/) * , , * [](https://freshrimpsushi.tistory.com/) * * [ ](https://datascienceschool.net/) * , , . . . * [ ](https://angeloyeo.github.io/2020/01/09/Bayes_rule.html) * , * [ ](https://tensorflow.blog/) * . . . * [ ](http://pythonkim.tistory.com/notice/25) * " 1" * [ ](https://subinium.github.io/) * * [LOVIT X DATA SCIENCE ](https://lovit.github.io/) * . . * [Google - Tensorflow Get Started ()](https://www.tensorflow.org/tutorials/) * Google document , Tensorflow * [Laon People - Machine Learning](https://laonple.blog.me/221196685472) * [ratsgo's blog](https://ratsgo.github.io/blog/categories/#natural-language-processing) * , . , . * [ ](https://hoya012.github.io/) * . . * [ - NLP](https://jiho-ml.com/) * , . * [ ](https://ratsgo.github.io/embedding/) * . . * [ - ](https://hoondongkim.blogspot.com/2019/03/recommendation-trend.html) * * [Team AI Korea](http://aikorea.org/blog/) * [AI Dev - ](http://aidev.co.kr/) * [TensorFlow ](https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/) * [Agustinus Kristiadi's Blog ()](https://wiseodd.github.io/page5/) * [Colah's Blog ()](http://colah.github.io/) * [ - ](https://teamdable.github.io/techblog/Reinforcement-Learning) ## (GitHub) **(Tutorial)** * [ repo - AIKorea.org](https://github.com/aikorea/cs231n) * github repo. * [Machine Learning with Python](https://github.com/tirthajyoti/Machine-Learning-with-Python) * Jupyter Notebook GitHub! * [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) * 10,000 PyTorch . * [Deep Learning (with PyTorch) by Atcold](https://github.com/Atcold/pytorch-Deep-Learning) * pytorch ipynb * [TensorFlow Example Source Code](https://github.com/aymericdamien/TensorFlow-Examples) * [ ()](https://github.com/tensorflow/docs-l10n/tree/master/site/ko) * , . * [ ](https://github.com/sjchoi86) * tensorflow . * [Tensorflow2.0 Tutorial - ](https://github.com/minsuk-heo/tf2) * TensorFlow 2.0 . * [Learning Python A.I Framework - jjerry-k](https://github.com/jjerry-k/learning_framework?fbclid=IwAR385K6J4Mgp3FsWfvCFaU6JMgOldoSadJo9iJLunSNghutOWJMOncrtCk4) * Tensorflow, PyTorch, MxNet ImageNet . * [Best of ML Python](https://github.com/ml-tooling/best-of-ml-python) * 840 ML * [CaptchaCracker](https://github.com/WooilJeong/CaptchaCracker) * Python Module * [Pretrained Language Models For Korean - kiyoungkim1](https://github.com/kiyoungkim1/LMkor) * Pretrained github **(Lecture)** * [ - Deep Learning Zero To All](https://github.com/hunkim/DeepLearningZeroToAll) * ( ) . * [deepLearningOpenLecture - ](https://github.com/eventia/deepLearningOpenLecture) * . **(Natural Language Processing** * [ ](https://github.com/ratsgo/embedding) * . . * [2 ](https://github.com/NLP-kr/tensorflow-ml-nlp-tf2) * 2 . * [ - ](https://github.com/kimwoonggon/publicservant_AI) * BERT, Transformer . (colab ) * [ - KB-ALBERT-KO](https://github.com/KB-Bank-AI/KB-ALBERT-KO) * ALBERT * [ Khaiii ](https://github.com/kakao/khaiii) * (Khaiii) * [ ](https://colab.research.google.com/drive/1FfhWsP9izQcuVl06P30r5cCxELA1ciVE?usp=sharing) * Colab . . * [Text Analysis - DSBA ](https://github.com/pilsung-kang/Text-Analytics) * . . * [TTS - mozilla](https://github.com/mozilla/TTS) * Deep learning for Text to Speech. Advanced Text-to-Speech generation . * [ aka. - ](https://github.com/kakaobrain/pororo) * PORORO: Platform Of neuRal mOdels for natuRal language prOcessing. all-in-one. ! **Computer Vision** * [Vision ](https://github.com/nh9k/Computer-vision) * Computer Vision OpenCV **Signal Processing** * [ ](https://github.com/biosignalsplux/biosignalsnotebooks) * (EEG), (ECG), (EMG) **GAN** * [Keras GAN](https://github.com/osh/KerasGAN) * Keras GAN * [Keras-DCGAN](https://github.com/jacobgil/keras-dcgan) * DCGAN Tutorial * [Keras-WGAN](https://github.com/tonyabracadabra/WGAN-in-Keras) * [ GAN ](https://github.com/rickiepark/GDL_code) * GAN GitHub repo . . * [Gan ZOO](https://github.com/hindupuravinash/the-gan-zoo) * GAN **** * [terryum - awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers) - * [Papers You Must Read (PYMR)](https://www.notion.so/c3b3474d18ef4304b23ea360367a5137?v=5d763ad5773f44eb950f49de7d7671bd) - Data Science & Business Analytics Lab () ** ** * [ (Effective Python) - ](https://github.com/gilbutITbook/006764) - * [Pandas, Numpy, Visualization - Python Data Science Handbook ](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb) - Python Data Science Handbook colab. Pandas, Numpy, Visualization . * [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) - () Python Data Science Handbook . 28K . * [ 2 - ](https://github.com/gilbutITbook/080228) - * [ with , (2020)](https://github.com/gilbutITbook/007017) - * [ ](https://github.com/rickiepark/handson-ml) - * [ ](https://github.com/wikibook/ml-definitive-guide) - . . * [Reinforcement Learning-2ndEdition by Sutton Exercise Solutions](https://github.com/LyWangPX/Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions) - Reinforcement Learning 2nd Edition (Original Book by Richard S. Sutton,Andrew G. Barto) . * [ ](https://github.com/lovedlim/tensorflow) - (2021) . . * [ 1 ](https://github.com/wikibook/dacon) - - 1 . ## (Web Sites) * [Toolify AI](https://www.toolify.ai/ko/Best-trending-AI-Tools) - AI , () . * [GPTers ](https://www.gpters.org/home) - ChatGPT . ChatGPT , ChatGPT . * [ ](https://developers.google.com/machine-learning/glossary/?hl=ko) - developer . * [pandas tutorial](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/index.html) - ( api ) * [20 minutes to matplotlib](https://www.tutorialdocs.com/article/python-matplotlib-tutorial.html) - 20 matplotlib ( api ) * [ CheatSheet ](https://graspcoding.com/cheat-sheet-for-python-machine-learning-and-data-science/) - python, pandas, numpy, matplotlib, seaborn CheatSheet * [Paper With Code](https://paperswithcode.com/) - . * [Codetorial](https://codetorial.net/?i=1) - numpy, matpoltlib, tensorflow . * [Keras Examples](https://keras.io/examples/) - example . 300 , . * [ 100](https://nlp100.github.io/ko/) - 100 * [(NLP) ](http://hero4earth.com/blog/learning/2018/01/17/NLP_Basics_01/) * [Machine Learning Mastery()](https://machinelearningmastery.com/) - . Python . * [Deep Note](https://deepnote.com/) - Jupyter Notebook Notebook. ! * [OpenAI Spinning Up](https://spinningup.openai.com/en/latest/) - OpenAI * [GUI for TensorFlow](https://www.perceptilabs.com/home) - GUI * [arXiv - ](https://arxiv.org/) - . , . * [arXiv sanity](https://arxiv.org/) - arXiv . * [PyTorch 5](https://docs.microsoft.com/en-us/learn/browse/?terms=pytorch) - Learn. , // * [PyTorch ()](https://tutorials.pytorch.kr/) - PyTorch * [PyTorch - ](https://kh-kim.gitbooks.io/pytorch-natural-language-understanding/content/) - PyTorch (Docs) ## (Wiki Docs) * [Dive into Deep Learning](https://ko.d2l.ai/) * , , , , . ! * [ ](https://wikidocs.net/book/1) * ! * [ 300](https://wikidocs.net/book/922) * 300 . * [Machine Learning ](https://wikidocs.net/book/587) * Andrew Ng . . * [PyTorch ](https://wikidocs.net/book/2788) * PyTorch * [ ](https://wikidocs.net/book/2155) * (). * [ ](https://wikidocs.net/book/2159) * . * [ ](https://wikidocs.net/book/110) * API Wiki! * [ - , ](https://wikidocs.net/book/2203) * , * [ - (scala), (spark) ](https://wikidocs.net/book/2350) * , * [ AI ](https://wikidocs.net/book/9451) - * AI * [ AI ](https://wikidocs.net/book/12852) - * , AI / ## (YouTube Channel) * [3Blue1Brown ](https://www.youtube.com/@3Blue1BrownKR) * 3Blue1Brown . !! * [SKPlanet TAcademy](https://www.youtube.com/channel/UCtV98yyffjUORQRGTuLHomw) * . * [ ](https://www.youtube.com/channel/UC9PB9nKYqKEx_N3KM-JVTpg) * . * [ - ](https://www.youtube.com/channel/UCpujNlw4SUpgTU5rrDXH0Jw) * , . * [ - ](https://www.youtube.com/channel/UC--LgKcZVgffjsxudoXg5pQ) * . * [ - Minsuk Heo](https://www.youtube.com/channel/UCxP77kNgVfiiG6CXZ5WMuAQ) * , PPT . * [ ](https://www.youtube.com/user/AngeloYeo/) * , . * [](https://www.youtube.com/channel/UCcbPAIfCa4q0x7x8yFXmBag) * , , . * [](https://www.youtube.com/channel/UCs7pXreQXz30-ENLsnorqdA) * . . * [](https://www.youtube.com/channel/UCt2wAAXgm87ACiQnDHQEW6Q) * . , , . * [StatQuest with Josh Starmer](https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw) * . * [Venelin Valkov](https://www.youtube.com/c/VenelinValkovBG/featured) * * [sentdex](https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ) * * [ EOStatistics](https://www.youtube.com/channel/UCVrs4KiLQz_gvVWWK1pKR1g) * . , . * [Upstage](https://www.youtube.com/channel/UCXJY5PPAToqqSketm5_PrDw) * , , (AI) . , . * [AI](https://www.youtube.com/channel/UC2L1DgDMD5pJ-35G47Objfw) * -- . 2 / . ## (YouTube) * [ PR12-season1](https://www.youtube.com/watch?v=auKdde7Anr8&list=PLWKf9beHi3Tg50UoyTe6rIm20sVQOH1br) * [ PR12-season2](https://www.youtube.com/watch?v=FfBp6xJqZVA&list=PLWKf9beHi3TgstcIn8K6dI_85_ppAxzB8) * [ PR12-season3](https://www.youtube.com/watch?v=D-baIgejA4M&list=PL_skMddDjnzq1wDI3t2cH9hlK6wBBapeA) * [ ](https://www.youtube.com/channel/UCDULrK2OJsiDhFroa2Aj_LQ) ## (Data Scientist Story) ** ** * [, , ? ft. - ](https://www.youtube.com/watch?v=-tmypCjhfkE) * [ () ? ft. - ](https://www.youtube.com/watch?v=PX4Kzoxdbgo) **Data Scientist** * [ , - Data Scientist](https://www.youtube.com/watch?v=7vk_cRUCk38&list=PLfi-4a2tMaHSPJ_a1m6lTgOCDQgNF945G) * [ , - Data Scientist](https://www.youtube.com/watch?v=3ue7nxqd7Ak&list=PLfi-4a2tMaHSPJ_a1m6lTgOCDQgNF945G&index=3) * [3 ? - Data Scientist](https://www.youtube.com/watch?v=-I8r_efiROU&list=PLfi-4a2tMaHSPJ_a1m6lTgOCDQgNF945G&index=2) **TP, ** * [ & ? !(ft. 10) - TP, ](https://www.youtube.com/watch?v=ykkBHGrBGPQ) * [ ? ? 10 !! - TP, ](https://www.youtube.com/watch?v=xBmycYVOO3Y) * [ ? ? ? 10 !! - TP, ](https://www.youtube.com/watch?v=nnHv8P21et8) ** DL bro** * [, , , ? - DL bro](https://www.youtube.com/watch?v=APS1bLYBUjg) ** ** * [ ](https://github.com/Team-Neighborhood/I-want-to-study-Data-Science) ## (Facebook Groups) * [TensorFlow Korea](https://www.facebook.com/groups/TensorFlowKR/?ref=bookmarks) * * [PyTorch KR](https://www.facebook.com/groups/PyTorchKR/) * * [Kaggle Korea](https://www.facebook.com/groups/KaggleKoreaOpenGroup/) * * [Recommender System KR](https://www.facebook.com/groups/2611614312273351/) * * [A.I. Lookbook](https://www.facebook.com/AI.Lookbook/) * * [AI Korea](https://www.facebook.com/groups/AIKoreaOpen/) * AI * [Reinforcement Learning KR](https://www.facebook.com/groups/ReinforcementLearningKR/) * * [](https://www.facebook.com/groups/statsas) * (Statistics Analysis Study) * [GNN KR](https://www.facebook.com/groups/2190093671090112/) * ## (Library) * [Tensorflow](https://www.tensorflow.org/?hl=ko) * * [PyTorch](https://pytorch.org/) * * [Scikit-learn](https://scikit-learn.org/stable/) * * [BindsNET](https://github.com/BindsNET/bindsnet) * for Pytorch * [NengoDL](https://github.com/nengo/nengo-dl) * for Tensorflow * [HpBandster](https://github.com/automl/HpBandSter) * - ## * [ ](https://www.bigdata-culture.kr/bigdata/user/main.do) * [PublicDataReader](https://github.com/WooilJeong/PublicDataReader) * Pandas DataFrame Python SDK * [ ](https://www.bigdata-map.kr) * [ ](https://data.seoul.go.kr/) * [Papers with Code|Datasets](https://paperswithcode.com/datasets) * [](https://www.data.go.kr/) * [Open Data Inception](https://opendatainception.io/) * [AI Hub](http://www.aihub.or.kr/) * AI , , , * [Appen](https://appen.com/resources/datasets/) * [ ](https://github.com/awesomedata/awesome-public-datasets) * [VisualData - Vision ](https://www.visualdata.io/) * [](http://lab.kdx.kr/adl/contest/main.php) * [Korpora: Korean Corpora Archives - ](https://github.com/ko-nlp/Korpora) * [KorQuAD2.0 - ](https://korquad.github.io/) * [ - ](https://corpus.korean.go.kr/) * [Microsoft Azure Dataset](https://azure.microsoft.com/ko-kr/services/open-datasets/catalog/) * [PhysioNet ](https://physionet.org/about/database/) ## * [ ](https://learnaday.kr/open-course/tfcert) ## * [ () - KIM TAE HEON](https://www.kaggle.com/agileteam/bigdatacertificationkr) * ## * [Kaggle ( )](https://teddylee777.github.io/linux/docker-kaggle-ko2/) * [(Python) / / (docker)](https://hub.docker.com/repository/docker/teddylee777/deepko) * [Udacity: Dog Breed Image Classifier in Pytorch](https://github.com/teddylee777/machine-learning) * [TED: Big Data playlist ( )](https://www.ted.com/playlists/56/making_sense_of_too_much_data) * (, )