​op free Data Science resources

1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html

2. Data Science Essentials
https://www.edx.org/course/data-science-essentials

3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse

4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI

5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY

6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM

7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/

8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx

10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE

🔗 Class Material

🎥 Семинар 3 - Time Series в акселерометрии: RIDI, IONet и RoNIN
👁 1 раз ⏳ 611 сек.
Запись на следующие семинары: http://mipt.ai/seminar
По всем вопросам пишите нам на info@mipt.ai
В этом мини-семинаре Тамаз Гадаев (исследователь группы Time Series Analysis Лаборатории машинного интеллекта) рассказывает про машинное обучение в задаче восстановления траектории объекта по IMU системе. Рассматриваются Robust IMU Double Integration(RIDI), IONet и Robust Neural Inertial Navagation in the Wild(RONIN).

https://vk.com/miptai - презентации и полезные материалы можно найти в нашей группе ВКонтакте

​A marriage of microscopy and machine learning

https://www.broadinstitute.org/news/marriage-microscopy-and-machine-learning

🔗 A marriage of microscopy and machine learning
Anne Carpenter and her lab are helping to transform cell imaging into a data science.

🎥 Choosing the best language for your machine learning solution: From R to Python | BRK2019
👁 2 раз ⏳ 2689 сек.
Creating a machine learning solution involves a number of complex decisions, from architecture, to algorithm selection, to the program and modeling language you choose. With varying capabilities, packages, and community support how do you know which language will best fit your need? This session introduces you to two of the most popular modeling languages - R and Python - and helps you understand

🎥 Марафон по HTML верстке 13-16 января. День 2. Как зарабатывать на фрилансе
👁 1 раз ⏳ 0 сек.
День 2. Вы узнаете, как зарабатывать на фрилансе, и получите пошаговый план. Сверстаем внутреннюю страницу сайта-порфолио.

Участвовать в марафоне: https://vk.com/app5898182_-40137828#u=837802&s=598630&force=1
Группа интенсива: https://vk.com/webstart1
Чат участников: https://vk.me/join/AJQ1d/UMVxZ4BuojBWW32OCV
Макет для 2-го дня: https://vk.cc/afcANs


::: О ПРОЕКТЕ :::::::::::::::::::::::::::::::::::::

Меня зовут Юрий Ключевский. Я занимаюсь разработкой сайтов уже много лет, специализируюсь на front-en

​Deep Learning for Person Re-identification: A Survey and Outlook

https://github.com/mangye16/ReID-Survey

ModeL: https://github.com/mangye16/Cross-Modal-Re-ID-baseline

Paper: https://arxiv.org/abs/2001.04193v1

🔗 mangye16/ReID-Survey
Deep Learning for Person Re-identification: A Survey and Outlook - mangye16/ReID-Survey

​How Does AI Detect Objects? (Technical)

🔗 How Does AI Detect Objects? (Technical)
Understand how Object Detection is applied and implemented using Machine and Deep Learning techniques

​Oxford (Real) Farming Conference 2020

🔗 Oxford (Real) Farming Conference 2020
NLP: Sentiment Analysis, Word Embeddings and Topic Modelling of 3,8K tweets

​Natural Image Matting via Guided Contextual Attention

Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area... (read more)

https://github.com/Yaoyi-Li/GCA-Matting

Paper: https://arxiv.org/abs/2001.04069v1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Yaoyi-Li/GCA-Matting
Natural Image Matting via Guided Contextual Attention - Yaoyi-Li/GCA-Matting

​Using neural networks to solve advanced mathematics equations

https://ai.facebook.com/blog/using-neural-networks-to-solve-advanced-mathematics-equations/

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Using neural networks to solve advanced mathematics equations
Facebook AI has developed the first neural network that uses symbolic reasoning to solve advanced mathematics problems.

​Run a TensorFlow SavedModel in Node.js directly without conversion

https://blog.tensorflow.org/2020/01/run-tensorflow-savedmodel-in-nodejs-directly-without-conversion.html

🔗 Run a TensorFlow SavedModel in Node.js directly without conversion

​Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

Paper: https://arxiv.org/abs/2001.03844v1

Code https://github.com/pfliu-nlp/Named-Entity-Recognition-NER-Papers

🔗 pfliu-nlp/Named-Entity-Recognition-NER-Papers
An elaborate and exhaustive paper list for Named Entity Recognition (NER) - pfliu-nlp/Named-Entity-Recognition-NER-Papers

​Solutions - KaggleDB

🔗 Solutions - KaggleDB

​Anomaly Detection with Autoencoders in TensorFlow 2.0
A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies.

🔗 Anomaly Detection with Autoencoders in TensorFlow 2.0
A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies.

🎥 Deep Learning for Anime & Manga, by Vie Jill-Jênn [INRIA & Mangaki]
👁 3 раз ⏳ 1379 сек.

​On the Relationship between Self-Attention and Convolutional Layers

https://github.com/epfml/attention-cnn

Paper: https://arxiv.org/abs/1911.03584v2

🔗 epfml/attention-cnn
Source code for "On the Relationship between Self-Attention and Convolutional Layers" - epfml/attention-cnn

​Mapping the tech world with t-SNE

🔗 Mapping the tech world with t-SNE
We analyse 200k tech news articles with the t-SNE algorithm

​Physical Theory of Intelligence

🔗 Physical Theory of Intelligence
v1.0

​An extensible Evolutionary Algorithm Example in Python

🔗 An extensible Evolutionary Algorithm Example in Python
Learning how to write an easy Evolutionary Algorithm from scratch in less than 50 lines of code that you can use for your projects.

🎥 DeepPhish: Simulating Malicious AI
👁 1 раз ⏳ 3054 сек.
91% of cybercrimes and attacks start with a phishing email. This means that cyber security researchers must focus on detecting phishing in all of its settings and uses. However, they face many challenges as they go up against sophisticated and intelligent attackers. As a result, they must use cutting-edge Machine Learning and Artificial Intelligence techniques to combat existing and emerging criminal tactics.

By Alejandro Correa Bahnsen

Full Abstract & Presentation Materials: https://www.blackhat.com/eu-1