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

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

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

Ukraine

Ukraine

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

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

Ukraine

• https://www.lyx.org/

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
• 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
• IEEE, International Conference on Data Mining

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

Ukraine

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

Ukraine

• - A curated list of awesome awesomeness
• - A curated list of awesome Deep Learning tutorials, projects and communities
• - The most cited deep learning papers

Ukraine

• Code

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

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

Ukraine

• - notebook на русском языке
• - into-machine-learning)) with Python Jupyter notebook and scikit-learn

Ukraine

Ukraine

• Machine Learning System Design

Ukraine

• Code

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

Ukraine

• - side scripting

Ukraine

• Kaggle
• UCI Machine Learning Repository

Ukraine

Ukraine