Machine learning

Machine Learning Practice: CoLab Interface

#027 ML Петр Ермаков. ODS - русскоязычное сообщество дата-сайентистов

#027 ML Petr Ermakov. ODS - Russian-speaking data scientist community

reading conditional independence from the BN

#026 ML Виталий Моисеев. Рекомендательные системы - как вырастить "счастье пользователей"

#026 ML Vitaly Moiseev. Recommender systems - how to grow "user happiness"

Gradient Descent | Neural Networks

Backpropagation Algorithm | Neural Networks

Sequential identification of agent reliability and the Bayes factor

#025 ML Про стажировки в Яндексе и распознавание рукописных цифр

#025 ML About internships at Yandex and handwritten digit recognition

Bayesian Networks independence I

Machine Learnia, La suite commence ici.

Machine Learnia, The sequel starts here.

#023 ML Ольга Перепелкина. Про федеративное обучение и распознавание эмоций

#023 ML Olga Perepelkina. About federated learning and emotion recognition

[Extract] The UK's Quantum Computer webinar ::: Alexei Kondratyev - Quantum Machine Learning

VC dimension as an adversary to learning

Support Vector Machine | Face Detection

VC dimension I

explaining PAC

A simple example of PAC learning

#021 ML Алексей Нечаев. AlphaGo и философия игры Го

#021 ML Alexey Nechaev. AlphaGo and the philosophy of the game of Go

#019 ML Евгений Разинков. Дорожная карта погружения в машинное обучение ч.2

#019 ML Evgeny Razinkov. Roadmap for diving into machine learning pt.2

#018 ML Евгений Разинков. Дорожная карта погружения в машинное обучение ч.1

#018 ML Evgeny Razinkov. Roadmap for diving into machine learning part 1

#017 ML Виктория Земляк. Machine Learning в научных исследованиях

#017 ML Victoria Zemlyak. Machine Learning in Scientific Research

#016 ML Линейная регрессия

#016 ML Linear Regression

#013 ML Алексей Ярошенко. Из специалиста по рекламе в дата-сайентисты. Учеба в MADE

#013 ML Alexey Yaroshenko. From advertising specialist to data scientist. Studies at MADE

#012 ML Юрий Яровиков. Как окончить ШАД, руководить Школой глубокого обучения и остаться в России

#012 ML Yuri Yarovikov. How to graduate from SHAD, lead the School of Deep Learning and stay in Russia

#010 ML Андрей Клименко. Neural Shit: Незаконное увеличение животных, Ветхий Путин и Програверование

#010 ML Andrey Klimenko. Neural Shit: Illegal animal enlargement, Frail Putin and Progroverovanie

Lecture 18 | MIT 6.881 (Robotic Manipulation), Fall 2020 | Reinforcement Learning (Part 2)

Lecture 17 | MIT 6.881 (Robotic Manipulation), Fall 2020 | Reinforcement Learning (Part 1)

#003 ML Методы машинного обучения

#003 ML Methods of Machine Learning

Curse of dimensionality

Comment CHOISIR LE BON MODÈLE de Machine Learning ?

How to CHOOSE THE RIGHT Machine Learning MODEL?

QSI Seminar: Dr Markus Heyl, Max Planck Inst., Reinforcement Learning for Digital Quantum Simulation

Learning Parity with Noise|| @ CMU || Lecture 26b of CS Theory Toolkit

Union bound and ML theory

QSI Seminar: Asst Prof Nathan Wiebe, PNNL, Training Fully Quantum Boltzmann Machines, 29/05/2020

MODÈLE DE MACHINE LEARNING - Covid19 (29/30)

MACHINE LEARNING MODEL - Covid19 (29/30)

QSI Seminar: Asst. Prof. Nana Liu, SJTU, Introducing Adversarial Quantum Learning, 22/05/2020

ENSEMBLE LEARNING : BAGGING, BOOSTING et STACKING (25/30)

ENSEMBLE LEARNING: BAGGING, BOOSTING and STACKING (25/30)

APPRENTISSAGE NON-SUPERVISÉ avec Python (24/30)

UNSUPERVISED LEARNING with Python (24/30)

FEATURE SELECTION avec SKLEARN (23/30)

FEATURE SELECTION with SKLEARN (23/30)

SKLEARN IMPUTER : NETTOYAGE DE DONNÉES

SKLEARN IMPUTE: DATA CLEANING