This AI Learns From Humans…and Exceeds Them

🎥 This AI Learns From Humans…and Exceeds Them
👁 1 раз ⏳ 255 сек.
The paper "Reward learning from human preferences and demonstrations in Atari" is available here:
https://arxiv.org/abs/1811.06521

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We would like

​instance weighted CE loss for PyTorch

https://gist.github.com/nasimrahaman/a5fb23f096d7b0c3880e1622938d0901

🔗 Pytorch instance-wise weighted cross-entropy loss
Pytorch instance-wise weighted cross-entropy loss. GitHub Gist: instantly share code, notes, and snippets.

​Deep Learning course: lecture slides and lab notebooks from Master Datascience Paris Saclay with a focus on applications

https://m2dsupsdlclass.github.io/lectures-labs/?fbclid=IwAR3V4f2UB9U9lwq9H_Vzr_E6gfUJJ_tDuXnsw0_459hCRVIjOAsm8B_Hd64

🔗 Deep Learning course: lecture slides and lab notebooks
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay

​A visual exploration of Gaussian Processes: beautiful interactive plots and a brief tutorial to make GPs more approachable

Link: https://www.jgoertler.com/visual-exploration-gaussian-processes/

#Statistics #GP #GaussianProcesses

🔗 A Visual Exploration of Gaussian Processes

Deep Learning Basics: Introduction and Overview - MIT

🎥 Deep Learning Basics: Introduction and Overview - MIT
👁 1 раз ⏳ 4087 сек.
An introductory lecture overviewing the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos visit our website or follow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/deep-learning-basics-slides
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:0

​Obtaining Calibrated Probabilities from Machine Learning Models

🔗 Obtaining Calibrated Probabilities from Machine Learning Models
A predictive model is well-calibrated if the probabilities that it predicts match the probability that it is correct. This lecture will summarize our current...

​DeepTraffic — new RL competition hosted by #MIT

Link: https://selfdrivingcars.mit.edu/deeptraffic/
Github: https://github.com/lexfridman/deeptraffic

#RL #selfdrivingcar

🔗

​How Uber predicts prices

Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

Link: https://eng.uber.com/neural-networks-uncertainty-estimation/

#RNN #LSTM #Uber

🔗

Machine Learning With TensorFlow | Neural Networks

🎥 Machine Learning With TensorFlow | Neural Networks | Part 1 | Eduonix
👁 1 раз ⏳ 554 сек.
Tensorflow is one of the most popular tools for building Machine learning solutions. In this tutorial, you will learn about Neural networks and deep learning implementation using Tensorflow. It is one of the most interesting but difficult concepts to grasp so pay attention. Let's get started!!

Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. http://bit.ly/2I

​Книги по Data Science \ Машинное обучение \ Нейронные сети \ Python

1. Python для сложных задач наука о данных и машинное обучение, Дж. Вандер Плас
2. Data Science. Наука о данных с нуля, Джоэл Грас
3. Введение в машинное обучение с помощью Python
4. Нейронные сети. Распознавание, управление, принятие решений, Барский А.Б.

📝 Плас Дж. Вандер - Python для сложных задач наука о данных и машинное обучение (Бестселлеры O'Reilly) - 2018.PDF - 💾12 530 081

📝 Грас Дж. Data Science. Наука о данных с нуля.pdf - 💾16 984 769

📝 Введение в машинное обучение с помощью Python [2017] physics_math.pdf - 💾12 079 480

📝 Барский - Нейронные сети распознавание управление принятие решений (2004)-1-1.pdf - 💾4 375 254

​Evaluating gambles using dynamics

Link: https://aip.scitation.org/doi/10.1063/1.4940236

#Statistics #Gambling

🔗 Evaluating gambles using dynamics
Gambles are random variables that model possible changes in wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but their generality limits predictive power. Expectation value maximizers are defined as rational in economics, but expectation values are only meaningful in the presence of ensembles or in systems with ergodic properties, whereas decision-makers have no access to ensembles, and the variables representing wealth in the usual growth models do not have the relevant ergodic properties. Simultaneously addressing the shortcomings of utility and those of expectations, we propose to evaluate gambles by averaging wealth growth over time. No utility function is needed, but a dynamic must be specified to compute time averages. Linear and logarithmic “utility functions” appear as transformations that generate ergodic ...

Taming False Discoveries with Empirical Bayes
https://towardsdatascience.com/taming-false-discoveries-with-empirical-bayes-2ce81aa8f407?source=collection_home---4------0---------------------

https://habr.com/post/435834/

EXAM — State-of-the-art метод классификации текста

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

Taming False Discoveries with Empirical Bayes
https://towardsdatascience.com/taming-false-discoveries-with-empirical-bayes-2ce81aa8f407?source=collection_home---4------2---------------------

​Lecture -12 : Advanced Machine Learning(ETH Zurich Spring 2018)

🔗 Lecture -12 : Advanced Machine Learning(ETH Zurich Spring 2018)
Professor -Dr. Joachim M. Buhmann Course Webpage - https://ml2.inf.ethz.ch/courses/aml/ Playlist - https://www.youtube.com/playlist?list=PLzn6LN6WhlN1x68-5GE...

​Бот генерирует учебники из статей Википедии
Всем известно, что Википедия — ценный информационный ресурс. Можно часами изучать тему, переходя от одной ссылки к другой для получения контекста по интересующему предмету. Но не всегда очевидно, как собрать весь контент по какой-либо одной общей теме. Например, как объединить все статьи по неорганической химии или истории средних веков, резюмируя самое важное? Примерно это попытались сделать Шахар Адмати и его коллеги из Бена-Гуриона в Негеве (Израиль), разработчики программы машинного обучения Wikibook-Bot.

🔗 Бот генерирует учебники из статей Википедии
Пример викиучебника (иллюстрация из научной статьи) Всем известно, что Википедия — ценный информационный ресурс. Можно часами изучать тему, переходя от одной...