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- h2o.ai h2o, sparkling water, steam
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- ml-workspace - All-in-one web-based IDE specialized for machine learning and data science
- 머신러닝 워크스페이스가 이미 구축된 도커 이미지를 제공하는 프로젝트. 모든것이 웹기반
- 라이브러리: Sklearn, TensorFlow, PyTorch, Keras 등등
- IDE: 주피터 노트북, 주피터 랩, VSCode
- 모니터링: TensorBoard, Netdata, Glances
- 버전컨트롤: git, Ungit
- 기타: 리모트 액세스, 리눅스 데스크탑 GUI 액세스, 다중 사용자 액세스, 포트 커스터마이징, 유용한 주피터노트북 확장
- 도커 이미지 하나로 모두 실행(GPU버전 기준, 단 현재는 CUDA10 만을 지원)
docker run -p 8080:8080 --gpus all mltooling/ml-workspace-gpu:latest- GPU 이외의 CPU 버전, R 특화 버전, 최소화 버전, 가벼운 버전등 도커 이미지 제공
- 위의 나열된 도구들이 제각각 노는것이 아님. 주피터 노트북과, 주피터 랩에는 유용한 확장이 설치
- 예를 들어, 원하는 디렉토리로 이동 후 git 버튼의 클릭으로 버전 컨트롤을 하거나, tensorboard 버튼의 클릭으로 텐서보드를 해당 폴더의 로그 파일을 기반으로 실행하거나, vscode 버튼의 클릭으로 선택한 파일을 VSCode에서 곧바로 편집이 가능
- 머신러닝 워크스페이스가 이미 구축된 도커 이미지를 제공하는 프로젝트. 모든것이 웹기반
- OpenML Home
- Petuum - a distributed machine learning framework
- PlaidML.pdf at main · ConstantPark/DL_Compiler
- PostgresML - an end-to-end machine learning solution
- Predict - see who will convert, before they do
- QLattice
- rc-data - Question answering dataset featured in "Teaching Machines to Read and Comprehend
- replicate: Version control for machine learning
- SynapseML: Simple and Distributed Machine Learning
- SystemML - IBM's SystemML Machine Learning - Now Apache SystemML http://systemml.apache.org
- traingenerator: 🧙 A web app to generate template code for machine learning
- Tribuo - A Java machine learning library
- WSO2 Machine Learner: Why would You care?
- SCALABLE MACHINE LEARNING - THE STATE OF DATAOPS / MLOPS IN 2018
- 머신러닝 오퍼레이션 자동화, MLOps
- MLOps 1. 실험 관리 AI 혁신 속도는 모델 학습에서 배포까지 걸리는 속도에 비례한다
- MLOps: Continuous delivery and automation pipelines in machine learning 번역
- Introducing MLOps - 차문수(Superb AI) :: 제33회 AWSKRUG DataScience모임 - YouTube
- What is MLOps? Machine Learning Operations Explained
- A Chat with Andrew on MLOps: From Model-centric to Data-centric AI - YouTube
- 앤드류 응 교수가 말하는 MLOps
- AI는 '빅데이터(Big Data)'보다 '굿데이터(Good Data)'를 좋아합니다 - 쉽고 재미있는 IT뉴스, 아웃스탠딩!
- 칼럼ㅣAI 프로젝트 악순환 고리 끊어라··· 'ML옵스' 마인드셋이 필요한 이유 - CIO Korea
- MLOps And Machine Learning Roadmap - KDnuggets
- 커피고래가 생각하는 MLOps | 커피고래의 노트
- 하나의 코드 베이스 & 파이프라인으로 여러 도메인에 “코드 수정 없이” AI 모델들을 배포할 수 있을까. 박중배 - PyCon Korea 2021 - YouTube
- Welcome to MLOps for ALL! - 모두의 MLOps
- MLOps를 공부하고자 하는 사람들의 지침서로 만든 프로젝트
- MLOps는 머신러닝을 서비스에 적용하기 더 쉽도록 파이프라인을 통해 머신러닝 엔지니어와 소프트웨어 엔지니어가 소통할 수 있게 함
- 개념 설명뿐 아니라 설정 방법과 Kubeflow에 대한 설명이 포함되어 있어서 MLOps에 관심 있거나 공부해 보고자 하면 아주 좋은 문서
- MLOps: Big Picture in GCP. “Why do we need different CI/CD for… | by Park Chansung | Google Developers Experts | Medium
- ML Ops with Dagster: 5 Key Features for Developing a Continuous Training Pipeline - Thinking Machines Data Science
- MODUCON 2021 글로벌 넘버원이 되기위한 MLOps Learning Path-박찬성Development Enviroment - 개발환경 - YouTube
- 모두의 MLOps (1) MLOps에 첫걸음을 내딛는 분들을 위한 지침서 | by Jongseob Jeon | We’re Team MakinaRocks! | Jan, 2022 | Medium
- 모두의 MLOps (2) MLOps의 단계와 단계별 핵심 기능 | by Jongseob Jeon | We’re Team MakinaRocks! | Mar, 2022 | Medium
- 모두의 MLOps (3) 머신러닝에서의 파이프라인이란. 모두의 MLOps 시리즈 3편. 이번 포스팅에서는 파이프라인이란… | by Jongseob Jeon | We’re Team MakinaRocks! | May, 2022 | Medium
- MLOps정의와 다양한 도구들-1 | Lablup Blog
- MLOps정의와 다양한 도구들-2 | Lablup Blog
- Why data scientists shouldn’t need to know Kubernetes
- MLOps란? MLOps 로드맵 및 MLOps 취업에 대한 생각 | MLOps 강의 추천 있음 - YouTube
- MLOps and DevOps: Why Data Makes It Different – O’Reilly
- Production ML: Getting Started with MLOps | by Hajar Khizou | Towards Data Science
- MLOps or How to Deploy Data Science at Scale | Towards Data Science
- MLOps 생태계 2022년 전망과 Backend.AI로 가속하는 하이퍼스케일 AI 실전로드맵 토크아이티 웨비나, 래블업 - YouTube
- MLOps : 딥랩 세미나 요약- 8회차. 💡 딥랩 세미나 요약은 발표자인 모더레이터의 발표를 듣고 사회자인… | by Eunsoo Park | 모두의연구소 기술 블로그 | May, 2022 | Medium
- INNOPOLIS AI SPACE-S 인공지능 세미나 - MLOps와 높은 진입 장벽 - YouTube
- FastAPI 및 TFServing을 이용한 ML 배포
- FastAPI 및 TFServing 으로 모델을 k8s(GKE) 환경에 배포하여, 몇 가지 성능을 확인해보는 간단한 프로젝트
- ml-deployment-k8s-fastapi: This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes
- ml-deployment-k8s-tfserving: This project shows how to serve an TF based image classification model as a web service with TFServing, Docker, and Kubernetes(GKE)
- FastAPI의 경우에 고려된 파라미터
- 단순 로컬에서 TF 모델과 TF -> ONNX 변환 모델 추론 속도
- FastAPI에는 ONNX로 변환된 모델을 넣었습니다
- uvicorn, gunicorn worker 의 개수
- TFServing의 경우에 고려된 파라미터
- 로컬/리모트 단일 추론에서 RestAPI 보다 gRPC가 뛰어남을 확인
- 커스텀 TFServing 이미지 빌드시, CPU 최적화된 TF를 컴파일
- TFServing 에 일부 CPU 코어 및 쓰레드 수에 따라 변경해볼만한 파라미터( --inter_op_parallelism_threads, --itra_op_parallelism_threads), TFServing 의 동적 배치 추론 기능 활성화 파라미터( --enable_batching 및 config 파일 작성)
- 두 경우에 모두 공통적으로 고려된 사항
- 서버를 구동하는 k8s 클러스터의 Pod 개수 (노드마다 여러 Pod이 있을 수 있으나, 각 노드별 하나의 Pod만 프로비져닝)
- 클러스터 Node 개수가 많아질 수록 Node의 사양은 ⬇, 클러스터 Node 개수가 적어질 수록 Node의 사양은 ⬆
- FastAPI로 ML 추론 서버를 구축하는 예제가 상당히 많이 존재하지만, 현실적으로는 상용 수준으로 쓰기에는 "어렵다"로 생각
- 불안정한 부분이 많고, 기본적으로 ML 추론용 서버가 갖춰야 할 다양한 기능이 결여되어 있기 때문에, 직접 이를 모두 구현해야 하는 골치아픈 작업이 발생(동적 배치 추론, 모델 버전 관리, 다중 버전의 다중 모델 동시 추론 기능 등)
- 두 저장소 모두 푸쉬, 새로운 모델 릴리즈에 따라 자동으로 GKE 클러스터로 도커 이미지를 빌드한 뒤 배포하는 GitHub Action 도 함께 작성
- nvidia-ml-py를 사용해서 kubernetes에 배포되어 있는 인스턴스에서 MIG 메모리 사용량 체크하기 | by Ryan Kim | Jun, 2022 | Medium
- AIQC: End-to-end deep learning on your desktop or server
- awesome-mlops: A curated list of references for MLOps
- Flyte ML Pipeline에 Flyte 도입하기. ML 오픈 소스의 망망대해 항해하며, ML Pipeline 구성해… | by Ryan Kim | Jun, 2022 | Medium
- MadeWithML: Learn how to responsibly deliver value with ML
- **MLOps-Basics
- MLOps Basics Week 0: Project Setup – Raviraja's Blog 프로젝트 설정
- MLOps Basics Week 1: Model Monitoring - Weights and Bias – Raviraja's Blog 모델 모니터링 - 웨이트와 편견
- MLOps Basics Week 2: Configurations - Hydra – Raviraja's Blog 구성 - 하이드라
- MLOps Basics Week 3: Data Version Control - DVC – Raviraja's Blog 데이터 버전 제어 - DVC
- MLOps Basics Week 4: Model Packaging - ONNX – Raviraja's Blog 모델 포장 - ONX
- MLOps Basics Week 5: Model Packaging - Docker – Raviraja's Blog 모델 포장 - Docker
- MLOps Basics Week 6: CI/CD - GitHub Actions – Raviraja's Blog CI / CD - GitHub 액션
- MLOps Basics Week 7: Container Registry - AWS ECR – Raviraja's Blog 컨테이너 레지스트리 - AWS ECR
- MLOps Basics Week 8: Serverless Deployment - AWS Lambda – Raviraja's Blog 서버리스 배치 - AWS Lambda
- MLOps Basics Week 9: Prediction Monitoring - Kibana – Raviraja's Blog 예측 모니터링 - 키바나
- MLOps Basics Week 10: Summary – Raviraja's Blog 요약**
- MLOps NYC
- wandb.ai Weights & Biases – Developer tools for ML MLOps 인프라
- Bare bones Python implementations of Machine Learning models and algorithms. Aims to cover everything from Data Mining techniques to Deep Learning
- Try Deep Learning in Python now with a fully pre-configured VM
- Simple Machine Learning Model in Python in 5 lines of code
- Your First Machine Learning Project in Python Step-By-Step
- 7 Steps to Mastering Machine Learning With Python
- TOP 5 ESSENTIAL BOOKS FOR PYTHON MACHINE LEARNING
- Machine learning with Python: A Tutorial
- facebook 바벨피쉬 middle learning 파이썬을 이용한 기계학습 알고리즘
- Ask HN: What is the best way to learn Machine Learning in Python?
- Searching for Approximate Nearest Neighbours
- Python, Machine Learning, and Language Wars. A Highly Subjective Point of View
- Example Machine Learning Notebook.ipynb
- linux machine learning
- Tutorial – Getting Started with GraphLab For Machine Learning in Python
- Machine Learning in Python has never been easier
- Essentials of Machine Learning Algorithms (with Python and R Codes)
- K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14
- Pycon2016_ML(DL)
- Machine Learning with Python
- Keynote: Machine Learning for Social Science | SciPy 2016 | Hanna Wallach
- Machine Learning: Python and the Power of Ensembles by Bargava Raman Subramanian
- Python Machine Learning Tutorial, Scikit-Learn: Wine Snob Edition
- Introduction To Machine Learning
- An Introduction to Python for Machine Learning with VS Code and Azure - YouTube
- An Introduction to Python Machine Learning with Perceptrons
- The Perceptron Algorithm explained with Python code
- 퍼셉트론 강의
- Simple Softmax Regression in Python — Tutorial
- 토끼와 거북이가 알려주는 회귀(Regression)
- Simple Linear Regression Tutorial for Machine Learning (ML) | by Towards AI Team | Towards AI | Medium
- 인공지능 컴퓨팅을 위한 파이썬 강좌
- Machine Learning Exercises In Python - Part 1
- Machine Learning Exercises In Python, Part 2
- Clustering with Scikit with GIFs
- Advice for applying Machine Learning
- Unsupervised Machine Learning for Fun & Profit with Basket Clusters
- Data Science – Deep Learning in Python
- 22 must watch talks on Python for Deep Learning, Machine Learning & Data Science (from PyData 2017, Amsterdam)
- Pattern Designing in Python | Pattern Matching in Machine Learning
- Some Essential Hacks and Tricks for Machine Learning with Python
- Supervised Learning with Python
- Unsupervised Learning with Python
- Machine Learning with Python: Easy and robust method to fit nonlinear data
- A Complete Machine Learning Project Walk-Through in Python
- A Complete Machine Learning Project Walk-Through in Python
- A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One
- 285+ Machine Learning Projects with Python | by Aman Kharwal | Coders Camp | Medium
- 파이썬으로 머신러닝 배우기 (1/3)
- 파이썬으로 머신러닝 배우기 (2/3)
- 파이썬으로 머신러닝 배우기 (3/3)
- TEAMLAB X Inflearn | 파이썬 머신러닝 입문 강좌
- 100 Days Of ML Code
- Deploying a Machine Learning Model as a REST API
- An Essential Guide to Numpy for Machine Learning in Python
- 4 Machine Learning Techniques with Python
- setscholars.net Applied Data Science in Business & Biological Sciences: Python, R & MATLAB Codes for Beginners
- Custom Transformers and ML Data Pipelines with Python
- How to Transform Research Oriented Code into Machine Learning APIs with Python ML에서의 refactoring에 대한 이야기
- 파이썬과 SQL을 활용한 패혈증(Sepsis) 분석
- 라이브러리 없이 단층 퍼셉트론 구하기 ML with pYTHON
- 사이킷 런의 svm.SVC 사용 및 흉내내어 만들어보기 ML with pYTHON
- Interpretable Machine Learning | LIME In Machine Learning
- Decision Trees in Machine Learning (ML) with Python Tutorial | by Towards AI Team | Towards AI | Nov, 2020 | Medium
- Know How to Create and Visualize a Decision Tree with Python
- Gradient Descent for Machine Learning (ML) 101 with Python Tutorial | Towards AI
- best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly
- 8 Basic Easy to Follow Steps to Learn Machine Learning with Python
- 파이썬, ML - SLiPP 스터디 - SLiPP::위키
- Start Asking Your Data “Why?” - A Gentle Introduction To Causal Inference | PyData Global 2021 - YouTube
- Sliding into Causal Inference, with Python! - Alon Nir | PyData Global 2021 - YouTube
- Building a Machine Learning Web Application Using Flask | by Gerry Christian Ongko | Feb, 2022 | Towards Data Science
- Top 20 Python Machine Learning Open Source Projects, updated
- Top 20 Python 머신러닝 오픈소스 프로젝트
- 10 Python Machine Learning Projects on GitHub
- 9 Python Analytics Libraries
- Top 20 Python Machine Learning Open Source Projects
- The Best Machine Learning Libraries in Python
- ann-writer - An artificial machine learning program that attempts to impersonate the writing style of any given text training set
- baal - Using approximate bayesian posteriors in deep nets for active learning
- Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML
- easyopt: zero-code hyperparameters optimization framework
- edenai-python: The best AI engines in one API: vision, text, speech, translation, OCR, machine learning, etc. SDK and examples for Python developers
- Gradient-Free-Optimizers: Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces
- igel: a machine learning tool that allows to train, test and use models without writing code
- JAX: Autograd and XLA
- ML 최적화 1. JIT & google JAX
- Flax: A neural network library for JAX designed for flexibility
- Haiku - a simple neural network library for JAX developed by some of the authors of Sonnet, a neural network library for TensorFlow
- RLax (pronounced "relax") - a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents
- ECE1513H: Introduction to Machine Learning Winter 2020 - LEC0101
- Intro to JAX: Accelerating Machine Learning research - YouTube
- Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia - Stochastic Lifestyle
- merlin: Kubernetes-friendly ML model management, deployment, and serving
- mindsdb: In-Database Machine Learning
- mljar-supervised: Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning
- Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks
- modal - Modular Active Learning framework for Python3
- NapkinML - Pocket-sized implementations of machine learning models in NumPy
- Neptune
- optuna: A hyperparameter optimization framework
- Orange - Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox
- PHOTONAI
- PyCaret - an open source, low-code machine learning library
- Announcing PyCaret 1.0.0 An open source low-code machine learning library in Python
- PyCaret튜토리얼-회귀.ipynb
- Introduction to Clustering in Python with PyCaret | by Moez Ali | Dec, 2021 | Towards Data Science
- A Practical Guide to ARIMA Models using PyCaret — Part 1 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
- A Practical Guide to ARIMA Models using PyCaret — Part 2 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
- A Practical Guide to ARIMA Models using PyCaret — Part 3 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
- PyCaret 2.3.5 Is Here! Learn What’s New | by Moez Ali | Nov, 2021 | Towards Data Science
- PyCM - Multi-class confusion matrix library in Python http://pycm.ir
- PyGAD Welcome to PyGAD’s documentation! — PyGAD 2.9.0 documentation
- PyLops — PyLops
- PyML - Python을 이용한 머신러닝 (코세리 인증과정 도전 스터디)
- Sacred - a tool to help you configure, organize, log and reproduce experiments
- scikit
- Dive into Machine Learning with ipython notebook and scikit-learn
- Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn
- Machine Learning with Scikit Learn | SciPy 2015 Tutorial | Andreas Mueller & Kyle Kastner Part I
- Machine Learning with Scikit Learn | SciPy 2015 Tutorial | Andreas Mueller & Kyle Kastner Part II
- Introduction to Machine Learning in Python with scikit-learn
- Scikit-Learn Cheat Sheet: Python Machine Learning
- Data science in Python: pandas, seaborn, scikit-learn
- Python Machine Learning: Scikit-Learn Tutorial
- Scikit-Learn Cheat Sheet: Python Machine Learning
- Learning to rank with Python scikit-learn
- Scikit-Learn Tutorial: Baseball Analytics in Python Pt 1
- Scikit-Learn Tutorial: Baseball Analytics in Python Pt 2
- Scikit-learn Tutorial
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction
- Machine Learning with Text in scikit-learn (PyCon 2016)
- Scikit Learn Multiclass Learning
- Hacking Scikit-Learn’s Vectorizers
- 8 ways to perform simple linear regression and measure their speed using Python
- Linear Regression Machine Learning Method Using Scikit-learn & Pandas in Python - Tutorial 30
- A beginner’s guide to Linear Regression in Python with Scikit-Learn
- 선형 회귀 모델에서 '선형'이 의미하는 것은 무엇인가?
- “파이썬 라이브러리를 활용한 머신러닝” 사이킷런 0.20 업데이트
- Scikit-Learn: A silver bullet for basic machine learning
- Scikit-Learn Library for Machine Learning in a Nutshell
- 파이썬 사이킷런(sklearn) 패키지에서 ROC곡선 쉽게 그리는 방법!
- Multiple Linear Regression and Visualization in Python 3D linear regression model 시각화
- PCA using Python (scikit-learn)
- 주성분 분석(PCA)의 이해와 실제 데이터(IRIS)에 적용
- 붓꽃 데이터는 이제 그만 - 차원축소 쉽게 이해하기 (PCA, LDA)
- ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn
- 파이썬(sklearn) 사이킷런(sklearn) 기초
- Scikit Learn을 이용한 분류와 회귀 머신러닝 with python - YouTube
- Deep Neural Multilayer Perceptron (MLP) with Scikit-learn | by Kaushik Choudhury | Sep, 2020 | Towards Data Science
- 사이킷런 0.24 맛보기! | 텐서 플로우 블로그 (Tensor ≈ Blog)
- Machine learning made easy with Python | Opensource.com
- 김도형: 파이썬 데이터 분석 3종 세트 - statsmodels, scikit-learn, theano - PyCon APAC 2016
- 사이킷런 해부학
- Intro — Scikit-learn course
- Scikit-Learn Course - Machine Learning in Python Tutorial - YouTube
- Scikit-learn Crash Course - Machine Learning Library for Python - YouTube
- Machine learning with missing values — Dirty data science
- How to Build Machine Learning Pipeline with Scikit-Learn? And Why is it essential? – Life With Data
- Adrin Jalali - Custom Scikit-learn Estimators - YouTube
- BayBiggies March 2021 Online Meeting: How to Reduce Scikit-Learn Training Time - YouTube
- scikit-learn with GPU! | 텐서 플로우 블로그 (Tensor ≈ Blog)
- scikit-learn-intelex Intel® Extension for Scikit-learn — Intel(R) Extension for Scikit-learn 2021.4 documentation
- scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python
- shap: A game theoretic approach to explain the output of any machine learning model
- shapash: Shapash makes Machine Learning models transparent and understandable by everyone
- shrynk - Using Machine Learning to learn how to Compress
- sklearn
- IACS_ComputeFest_sklearn
- hyperopt-sklearn Hyper-parameter optimization for scikit-learn
- Python: Implementing a k-means algorithm with sklearn
- Speeding Up Sklearn’s kNN Algorithms With Custom Distance Metrics Written In Cython
- Implementing a Neural Network from Scratch in Python – An Introduction
- Building Prediction APIs in Python (Part 1): Series Introduction & Basic Example
- Finding the right model parameters
- Feature Selection with sklearn and Pandas
- SMOTE를 통한 데이터 불균형 처리 oversampling 기법의 하나
- Two hours later and still running? How to keep your sklearn.fit under control
- sklearn의 train_test_split() 사용법
- from sklearn import *
- Grid search for parameter tuning. Learn this easy and simple technique to… | by Magdalena Konkiewicz | Oct, 2020 | Towards Data Science
- Feature engineering package with sklearn like functionality
- Feature-engine
- 장점
- 사용 방법 자체는 sklearn과 같이 fit(), transform()으로 사용 가능
- 캐글에서 사용하는 다양한 테크닉이 포함되어 있어 Pandas에서 Scratch로 구현하기 어려운 분들에게 도움
- sckit-learn에서는 data transform을 하기 위해
- as-is: pd.Series로 전달하여 np.array로 결과를 반환하기에 다시 table에 합치는 과정이 일부 번거로움
- to-be: 이 라이브러리는 pd.DataFrame을 전달, pd.DataFrame을 반환해주어 번거로움 감소
- 아쉬운 점
- 파라미터명이 기존 라이브러리와 매칭되지 않는 부분
- 기존 pandas는 coloums 또는 cols로 열을 표현한다면 이 라이브러리에서는 variables라는 명칭 사용
- Feature Creation에서 combination할때 커스텀 함수 기능 부재. 현재는 사칙연산 정도만 제공
- time-series data / text data feature engineering이 추가된다면 확실히 범용적인 툴이 될 수 있을 것 같다는 기대
- slr - Simple linear regression with confidence intervals on parameters and prediction
- smalltrain
- smile - Statistical Machine Intelligence & Learning Engine http://haifengl.github.io/smile
- Streamlit - the first app framework specifically for Machine Learning and Data Science teams
- awesome-streamlit: The purpose of this project is to share knowledge on how awesome Streamlit is and can be
- Streamlit - 파이썬 코드를 커스텀ML도구로 쉽게 만들기 | GeekNews
- Streamlit 101: An in-depth introduction
- KR 파이썬 웹어플리케이션 맛보기 (feat. Streamlit) | by SEO, Wonyoung | Medium
- How to Build a Simple Machine Learning Web App in Python
- NotebookToWebApp/article.md at master · ChristianFJung/NotebookToWebApp
- 멍개의 퇴근후 공부 파이썬 streamlit 다뤄보기 - YouTube
- Build 12 Data Science Apps with Python and Streamlit - Full Course - YouTube
- Python Streamlit 사용법 - 프로토타입 만들기 · 어쩐지 오늘은
- 스트림릿, 머신러닝 웹개발을 쉽고 빠르게 – techNeedle 테크니들
- Deploying a Python machine learning app on Kubernetes - Ketch
- Getting Started with Steamlit for Python - Build a Functioning Web App in Minutes - YouTube
- Streamlit Tutorials - YouTube
- streamlit-google-oauth: An example Streamlit application that incorporates Google OAuth 2.0
- TensorDash A Must-Have Tool for Every Data Scientist | by Arun | Oct, 2020 | Towards Data Science
- tpot - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. http://rhiever.github.io/tpot
- Uber
- Machine Learning with R: An Irresponsibly Fast Tutorial
- Essentials of Machine Learning Algorithms (with Python and R Codes)
- Image Manipulation for Machine Learning in R
- glouppe/phd-thesis - Repository of my thesis "Understanding Random Forests"
- Categorical Variable Encoding and Feature Importance Bias with Random Forests
- Random Forests - Leo Breiman and Adele Cutler
- The Unreasonable Effectiveness of Random Forests
- 군중은 똑똑하다 — Random Forest
- Random Forest
- The Random Forest Algorithm
- Improving the Random Forest in Python Part 1
- Hyperparameter Tuning the Random Forest in Python
- Random Forest in Python - A Practical End-to-End Machine Learning Example
- An Introduction to Random Forest using the fastai Library (Machine Learning for Programmers — Part 1)
- An Intuitive Guide to Interpret a Random Forest Model using fastai library (Machine Learning for Programmers – Part 2)
- How to use RandomForest Classifier and Regressor in Python
- An Implementation and Explanation of the Random Forest in Python
- 랜덤포레스트가 뭐길래? 회의에서 당당하게, 수식없이 알아보자
- 3.3 1/4 뉴스 토픽 분류 랜덤포레스트 교차검증 학습예측데이터셋 전처리와 나누기 - YouTube
- 3.3 2/4 뉴스 토픽 분류 랜덤포레스트 교차검증 TF-IDF 로 벡터화 하기 - YouTube
- 3.3 3/4 뉴스 토픽 분류 랜덤포레스트 교차검증 데이콘에 제출에 보기 전에 점수를 미리 알아보는 방법 - YouTube
- 3.3 4/4 뉴스 토픽 분류 랜덤포레스트 교차검증 데이콘에 제출하기 - YouTube
- OpenAI Gym BETA - A toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go
- REINFORCEMENT LEARNING PART 1: Q-LEARNING AND EXPLORATION
- Reinforcement Learning 1 - Expected Values
- Deep Reinforcement Learning: Pong from Pixels
- Reinforcement Learning Neural Turing Machines
- Language Understanding for Text-based Games Using Deep Reinforcement Learning
- Giraffe: Using Deep Reinforcement Learning to Play Chess
- Guest Post (Part I): Demystifying Deep Reinforcement Learning
- 강화학습 튜토리알 - 인공 신경망으로 '퐁' 게임을 학습시키자 (Andrej Karpathy 포스트 번역)
- Reinforcement Learning 그리고 OpenAI - 1: OpenAI를 위한 개발환경 구축
- Reinforcement Learning 그리고 OpenAI - 2: CartPole예제 이해하기
- Reinforcement Learning 그리고 OpenAI - 3: CartPole and Deep Q Learning (1) DQN(Deep Q-Networks)
- Reinforcement Learning
- RLlib: Scalable Reinforcement Learning — Ray v1.5.2
- KeystoneML - Machine Learning Pipeline
- Feature Engineering at Scale With Spark
- Audience Modeling With Spark ML Pipelines
- Apache Spark로 시작하는 머신러닝 입문
- Kernel-SVM
- Support Vector Machine (SVM) Classification
- SVM: The go-to method machine learning algorithm
- Detection of Leaf and Weed using SVM | Machine Learning | Ruchi Mehra
- ThunderSVM: A Fast SVM Library on GPUs and CPUs
- 초짜 대학원생의 입장에서 이해하는 Support Vector Machine (1)
- 서포트벡터머신Support Vector Machine을 위한 비선형 계획 문제의 쌍대정리
- Support Vector Machine — Introduction to Machine Learning Algorithms
- How to unit test machine learning code
- Effective testing for machine learning systems
- Effective Testing for Machine Learning Projects - Eduardo Blancas | PyData Global 2021 - YouTube
- drifter_ml - ML Testing
- Tutorial
- Practical Theano Tutorial
- brutally short intro to theano word embeddings
- practice - 윈도우에서 Theano 설치하기
- Deep Learning, Jumpto with Theano
- jaeho-kang/deep-learning
- Faster deep learning with GPUs and Theano
- Computation Graph Toolkit - I would like to announce a library that I have been working on with a few collaborators1, called the Computation Graph Toolkit (CGT)
- SymPy and Theano -- Code Generation
- SymPy and Theano -- Scalar Simplification
- SymPy and Theano -- Matrix Expressions
- Recurrent Neural Networks Tutorial, Part 2 – Implementing a RNN with Python, Numpy and Theano
- Practical Theano Tutorial
- Linear Regression In Theano
- Linear Regression(with c++)
- Logistic Regression(1)
- Multivariable Linear Regression(with c++)
- Linear Regression
- Theano-Example-James-010.ipynb
- IPython Theano Tutorials
- SPEEDING UP YOUR NEURAL NETWORK WITH THEANO AND THE GPU
- Installation of Theano on Windows
- Theano Tutorials - Ian Goodfellow
- Theano Yellow Fin
- theano-tutorial
- IPython Theano Tutorials
- Torch
- Awesome Torch - A curated list of awesome Torch tutorials, projects and communities
- A DSL for deep neural networks, supporting Caffe and Torch http://ajtulloch.github.io/dnngraph
- neural-style - Torch implementation of neural style algorithm
- Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
- Recurrent Model of Visual Attention
- 토치(Torch7) 시작하기
- Torch 7 Deep Learning Installation (Ubuntu 14.04)
- Deep Learning with Torch The Long way of Deep Learning with Torch
- Understanding Natural Language with Deep Neural Networks Using Torch
- Generating Faces with Torch
- Lighting the way to deep machine learning
- github.com/torchnet/torchnet
- TorchCraft - Connecting Torch to StarCraft
- RNN(Recurrent Neural Network)과 Torch로 발라드곡 작사하기
- High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
- 토치 학습 자료 한국어 번역본
- Facebook AI Research Sequence-to-Sequence Toolkit
- Transfer Learning - Machine Learning's Next Frontier
- Transfer Learning - Machine Learning's Next Frontier
- Paper is out; Transfer learning for music classification and regression tasks, and behind the scene, negative results, etc
- youtube.com/user/dvbuntu/featured Self-drinving car Transfer Learning Model
- "Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping"
- Transfer Learning is the new frontier. TensorFlow might help implement transfer learning
- Domain Adaptation for Visual Applications: A Comprehensive Survey
- Transfer Learning 의 일종인 Domain Adaptation 에 대한 방대한 리뷰
- TRANSFER LEARNING FOR SOUND CLASSIFICATION
- Inception v3를 활용한 Transfer Learning
- Transfer Learning using differential learning rates
- Taskonomy: Disentangling Task Transfer Learning 리뷰
- cs231n.github.io/transfer-learning
- Hands-On Transfer Learning with Python
- Machine Learning Field Guide
- Machine Learning Tutorials - YouTube
- Getting machine learning to production · Vicki Boykis
- Markus Loning - Introduction to Machine Learning with Time Series | PyData Fest Amsterdam 2020 - YouTube
- Machine Learning Algorithms For Beginners with Code Examples in Python
- Machine Learning by Analogy
- 40+ Modern Tutorials Covering All Aspects of Machine Learning - Data Science Central
- 머신러닝야학
- How to Learn Machine Learning Online Free in 2021?- Free Resources
- 문제해결 관점으로 머신러닝 이해하기 - YouTube