Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Machine-Learning
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
https://github.com/ElizaLo/Machine-Learning
Last synced: about 23 hours ago
JSON representation
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📚 Books
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Boris Mirkin
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - D.Barber (2015)
- - Jiawei Han et. al.
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Carl E. Rasmussen, Christopher K. I. Williams
- - P. Flach (2012)
- - C.M.Bishop (2006)
- - Peter Harrington
- - Richard S. Sutton, Andrew G. Barto
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Tom Mitchell
- - Hal Daumé III ([another link](http://ciml.info))
- - M.J.Zaki, W.Meira Jr (2014)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Nada Lavrac, Saso Dzeroski
- - Nils J Nilsson (1997)
- - D. Michie, D. J. Spiegelhalter
- - C.M.Bishop (2006)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
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Big Data
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Ukraine
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Big Data Fundamentals: Concepts, Drivers & Techniques
- Big Data: Principles and best practices of scalable realtime data systems
- Big Data Specialization
- Data Scientist vs Data Engineer
- Data Engineer VS Data Scientist
- Data Scientist vs Data Engineer
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
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🔹 Online Courses
- Бесплатные курсы для студентов
- Эконометрика - недельный курс от ВШЭ
- Customer Analytics
- Data Analyst Nanodegree
- Coursera Together: Free online learning during COVID-19
- Intro to Machine Learning - to-end process of investigating data through a machine learning lens
- Data Science and Engineering with Spark
- Get Started with Data Science Foundations
- Machine Learning
- Data Science
- Бесплатные курсы для изучения навыков в области облачных технологий
- Machine Learning Foundations: A Case Study Approach
- DeepLearning.AI
- Эконометрика - недельный курс от ВШЭ
- Practical Predictive Analytics: Models and Methods
- Grow.Google
- LearnDigital.WithGoogle
- Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses
- Calculus One
- Машинное обучение - obucheniye)
- Calculus: Single Variable Part 1
- Introduction to Recommender Systems
- Machine Learning
- Machine Learning Engineer Nanodegree
- Python Knowledge Map
- Machine Learning Crash Course with TensorFlow APIs - Google's fast-paced, practical introduction to machine learning
- Technical Writing Courses
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Code editors
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Ukraine
- SublimeREPL - запускает `Read-eval-print loop` в соседней вкладке, удобно для пошаговой отладки кода
- - VIM XXI века*;, отлично подходит для python, если использовать вместе с плагинами:
- Package Control - для быстрой и удобной работы с дополнениями
- Git - для работы с git
- Jedi - делает автодополнения для Python более умными и глубокими
- Auto-PEP8 - приводит код в соответствие с каноном стиля *pep8*
- Python Checker - проверка кода
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Reinforcement Learning
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Ukraine
- Introduction to Reinforcement learning with David Silver (DeepMind)
- David Silver's Reinforcement Learning Course (UCL, 2015)
- Reinforcement Learning: An Introduction (2nd Edition)
- Course website
- CS294-112 - Deep Reinforcement Learning (UC Berkeley)
- CS885 - Reinforcement Learning (UWaterloo), Spring 2018
- Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- Deep Reinforcement Learning Slides @ NIPS 2016
- OpenAI Spinning Up
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Table of Contents
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🎓 Courses
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Machine Learning Map
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Linear Algebra
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Theory of Probability and Mathematical Statistics
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Neural Networks
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Ukraine
- - то иначе охарактеризовать Джеффри Хинтона (человека, стоящего у истоков современных подходов к обучению нейросетей с помощью алгоритма обратного распространения ошибки) сложно. Курс у него получился отличный»
- - книга по нейросетям и глубинному обучению ([:octocat: repo on github](https://github.com/mnielsen/neural-networks-and-deep-learning))
- - week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision
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LaTeX
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Ukraine
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🟥 YouTube
- Google Cloud Platform: AI Adventures
- Lviv Data Science Summer School 2020 lectures
- Samsung AI Innovation Campus - Russia
- Machine Learning University - samples/aws-machine-learning-university-accelerated-nlp)
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R
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Conferences
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International
- AAMAS, International Conference on Autonomous Agents and Multi-Agent Systems
- ICCBR, International Conference on Case-Based Reasoning
- SIAM, Society for Industrial and Applied Mathematics
- SIGKDD, Conference on Knowledge Discovery and Data Mining
- ACL, Association for Computational Linguistics
- EMNLP, Empirical Methods in Natural Language Processing
- IJCNLP, International Joint Conference on Natural Language Processing
- The Data Science Conference
- Strata Data & AI Conference
- useR!
- MDS, Conference on Mathematics of Data Science
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North America
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Europe
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Ukraine
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▶️ Websites
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:octocat: GitHub Repositories
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Ukraine
- 100-best-github-machine-learning
- Machine learning cheat sheet - soulmachine (2015)|
- курса «Математика и Python» - recommendations.md)|
- Литература для поступления в ШАД
- 100-best-github-machine-learning
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Awesome List
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📌 Other
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Ukraine
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Deploy Machine Learning Model to Production
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Ukraine
- How to deploy Machine Learning models as a Microservice using FastAPI
- How to deploy Machine Learning models as a Microservice using FastAPI
- Почему Вы должны попробовать FastAPI?
- How to deploy Machine Learning models as a Microservice using FastAPI
- How to deploy Machine Learning models as a Microservice using FastAPI
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What's is the difference between _train, validation and test set_, in neural networks?
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Ukraine
- Understanding GRU Networks
- Early stopping
- Code
- Base text - **Alice in Wonderland**
- Code
- Code
- Code
- Understanding GRU Networks
- Code
- SMS Spam Collection Dataset
- Code
- Formatted text of **Alice in Wonderland**
- implementation - character inputs as described in the original paper and improving [GauthierDmns' code](https://github.com/GauthierDmn/question_answering).
- Paper
- Used Articles
- Understanding GRU Networks
- Understanding GRU Networks
- MNIST Database
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Python, IPython, Scikit-learn etc.
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Algorithms
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Machine Learning System Design
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Ukraine
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Bayesian Statistics
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Ukraine
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Causal Inference
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Ukraine
- A Crash Course in Causality: Inferring Causal Effects from Observational Data
- Powerful Concepts in Social Science
- MIT Statistics and Data Science Center, 2017
- Conference on Cognitive Computational Neuroscience 2019
- Machine Learning Summer School 2020
- Machine Learning Summer School 2013
- Causal Inference 3: Counterfactuals
- Causality for Machine Learning, Bernhard Schölkopf, 2019
- Elements of Causal Inference
- Causal Structure Learning,Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen, 2017
- Causal inference in statistics: An overview, 2009
- JUDEA PEARL, MADELYN GLYMOUR, NICHOLAS P. JEWELL CAUSAL INFERENCE IN STATISTICS: A PRIMER
- JUDEA PEARL - CAUSALITY, 2nd Edition, 2009
- Causation, Prediction, and Search, Second Edition
- Learning DAGs with Continuous Optimization
- Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
- Active Invariant Causal Prediction: Experiment Selection through Stability, Juan L Gamella, Christina Heinze-Deml, 2020
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods, 1969
- Fast Greedy Equivalence Search (FGES) Algorithm for Continuous Variables
- Greedy Fast Causal Inference (GFCI) Algorithm for Continuous Variables
- Causal Decision Trees
- Discovery of Causal Rules Using Partial Association
- Causal Inference in Data Science From Prediction to Causation
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JavaScript-libraries for visualizing
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📑 Open Datasets list
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Ukraine
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Reddit
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Social Networks (chanels, chats, groups, etc.)
Programming Languages
Categories
📚 Books
47
🔹 Online Courses
33
Theory of Probability and Mathematical Statistics
23
Causal Inference
23
🎓 Courses
20
Big Data
19
What's is the difference between _train, validation and test set_, in neural networks?
18
Conferences
17
📌 Other
16
Python, IPython, Scikit-learn etc.
15
Reinforcement Learning
13
Code editors
11
Linear Algebra
8
Reddit
8
Awesome List
7
Neural Networks
7
LaTeX
6
R
6
Table of Contents
5
Deploy Machine Learning Model to Production
5
Social Networks (chanels, chats, groups, etc.)
5
:octocat: GitHub Repositories
5
🟥 YouTube
4
Algorithms
3
JavaScript-libraries for visualizing
2
▶️ Websites
2
📑 Open Datasets list
1
Bayesian Statistics
1
Machine Learning System Design
1
Machine Learning Map
1
Sub Categories
Keywords
python
6
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5
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5
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4
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3
neural-network
3
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2
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2
nlp
2
nlp-machine-learning
2
data-structures
2
jupyter-notebook
2
computer-science
2
coursera-machine-learning
1
awesome-ml
1
awesome-machine-learning
1
courses
1
awesome-lists
1
plotly
1
deep-learning-tutorial
1
awesome-data-science
1
python-resources
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python-library
1
python-framework
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collections
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1
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face-images
1
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1
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1
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1
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1