Ukraine

• - A curated list of awesome Deep Learning tutorials, projects and communities
• - The most cited deep learning papers
• - A curated list of awesome awesomeness
• - Directory of Python books
• - A curated list of awesome Deep Learning for Natural Language Processing resources
• - Curated list of Python resources for data science
• - A curated list of awesome adversarial machine learning resources
• - A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations
• - Papers on Graph neural network (GNN)
• - Awesome free machine learning and AI courses with video lectures
• - Curated list of reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world
• - awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc
• - A curated list of awesome Deep Reinforcement Learning resources
• - A curated list of awesome big data frameworks, ressources and other awesomeness
• - quality open datasets in public domains (on-going)
• - Probably the best curated list of data science software in Python

Ukraine

• - 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
• SublimeREPL - запускает `Read-eval-print loop` в соседней вкладке, удобно для пошаговой отладки кода
• Auto-PEP8 - приводит код в соответствие с каноном стиля *pep8*
• Python Checker - проверка кода
• - - Style Guide for Python Code.

Ukraine

• https://www.lyx.org/
• http://www.texstudio.org/

Ukraine

Ukraine

• - into-machine-learning)) with Python Jupyter notebook and scikit-learn
• - notebook на русском языке
• - learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
• - руководство по нюансам языка, мимо которых часто проходят новички (автор — Yasoob Khalid);

Ukraine

Ukraine

Ukraine

• David Silver's Reinforcement Learning Course (UCL, 2015)
• Reinforcement Learning: An Introduction (2nd Edition)
• CS294-112 - Deep Reinforcement Learning (UC Berkeley)
• Course website
• 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
• dennybritz/reinforcement-learning
• CS 8803 - Reinforcement Learning (Georgia Tech)
• 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)
• John Schulman - Deep Reinforcement Learning (4 Lectures)
• Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
• Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
• John Schulman - Deep Reinforcement Learning (4 Lectures)

Ukraine

• Code

Ukraine

• - 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
• - книга по нейросетям и глубинному обучению ([:octocat: repo on github](https://github.com/mnielsen/neural-networks-and-deep-learning))
• - 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»
• - 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
• - книга по нейросетям и глубинному обучению ([:octocat: repo on github](https://github.com/mnielsen/neural-networks-and-deep-learning))
• - domain question answering;

Ukraine

• Kaggle
• Google’s Public Data Sets
• Google’s Public Data Sets

Ukraine

• - T. Hastie, R. Tibshirani, J. Friedman
• - test, ANOVA, корреляция, регрессия и др.)
• - T. Hastie, R. Tibshirani, J. Friedman

Ukraine

• Big Data Specialization
• Big Data Fundamentals: Concepts, Drivers & Techniques
• Big Data: Principles and best practices of scalable realtime data systems
• 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
• Data Engineer VS Data Scientist
• Data Scientist vs Data Engineer
• Data Engineer VS Data Scientist

Ukraine

• A Crash Course in Causality: Inferring Causal Effects from Observational Data
• JUDEA PEARL, MADELYN GLYMOUR, NICHOLAS P. JEWELL CAUSAL INFERENCE IN STATISTICS: A PRIMER
• Causal inference in statistics: An overview, 2009
• 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
• 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
• Powerful Concepts in Social Science
• Machine Learning Summer School 2013
• Powerful Concepts in Social Science
• 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
• Causal Decision Trees

Ukraine

• - side scripting

Ukraine

• Tutorials

Ukraine

• - org/mlr)) — Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions;

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
• MDS, Conference on Mathematics of Data Science
• 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!
• ICCBR, International Conference on Case-Based Reasoning
• ICML, International Conference on Machine Learning
• Open Data Science Conference
• SIAM, Society for Industrial and Applied Mathematics

North America

• NAACLP, North American Chapter of the Association for Computational Linguistics

Europe

• ECML, European Conference on Machine Learning
• EACL, European Chapter of the Association for Computational Linguistics

Ukraine

• Data Science UA Conference
• AI Ukraine
• Data Science fwdays
• AI Ukraine

Ukraine

• 100-best-github-machine-learning
• курса «Математика и Python» - recommendations.md)|
• Литература для поступления в ШАД
• Machine learning cheat sheet - soulmachine (2015)|
• trekhleb, homemade-machine-learning
• ml-surveys
• Top-down learning path: Machine Learning for Software Engineers
• trekhleb, machine-learning-experiments
• 100-Days-Of-ML-Code
• data-science-blogs
• ml-course-msu
• trekhleb, machine-learning-octave
• Machine Learning Guide
• Machine_Learning_and_Deep_Learning
• Probabilistic Programming and Bayesian Methods for Hackers
• Machine Learning Notebooks
• MachineLearning_DeepLearning

Ukraine

• Code

Ukraine

• Machine Learning System Design

Ukraine

• 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
• 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?

Ukraine

Ukraine

• Early stopping
• Code
• MNIST Database
• Code
• Code
• Code
• Understanding GRU Networks
• Code
• SMS Spam Collection Dataset
• Code
• Base text - **Alice in Wonderland**
• 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
• Understanding GRU Networks
• Understanding GRU Networks
• Understanding GRU Networks
• Code
• Subset of MNIST
• SMS Spam Collection Dataset
• Code
• Base text - **Alice in Wonderland**
• Formatted text of **Alice in Wonderland**
• Paper
• Used Articles