​DiffTaichi: Differentiable Programming for Physical Simulation

https://github.com/yuanming-hu/difftaichi

https://arxiv.org/abs/1910.00935v2

🔗 yuanming-hu/difftaichi
10 differentiable physical simulators built with Taichi differentiable programming (DiffTaichi, ICLR 2020) - yuanming-hu/difftaichi

Лекции по Big Data
1 - BigData. Введение в машинное обучение
2 - BigData. Python
3 - BigData. Что такое BigData
4 - BigData. OLAP. What and why
5 - BigData. IoT и BigData
6 - BigData. Сhallenges of classification
7 - BigData. Formal Context Analysis
8 - BigData. Регрессия
9 - BigData. Хранение и анализ больших данных
10 - BigData. Deep learning
#BigData

#video

🎥 01. 1 - BigData. Введение в машинное обучение
👁 3729 раз ⏳ 1959 сек.


🎥 2 - BigData. Python
👁 103 раз ⏳ 8499 сек.
Лекция 2 - Python, как язык анализа данных.
В лекции сделан небольшой обзор языков и программ для анализа данных. Рассказан базовый синтаксис языка...

🎥 3 - BigData. Что такое BigData
👁 38 раз ⏳ 3792 сек.
Лекция 3 - Что такое BigData?
В лекции рассказывается о том, что же это такое. Цели, проблемы и практическая польза результатов
анализа BD на приме...

🎥 4 - BigData. OLAP. What and why
👁 29 раз ⏳ 5766 сек.
Лекция 4 - OLAP. What and why. Lightning talk.
В лекции описание OLAP. Что это? Для чего? Каковы отличия от OLTP? Небольшой экскурс в анализ данных...

🎥 5 - BigData. IoT и BigData
👁 16 раз ⏳ 4183 сек.
Лекция 5 - IoT and BigData
В лекции рассказывается о IoT and BigData. Области их пересечения, применения, основные проблемы и методы решения. Lambd...

🎥 6 - BigData. Сhallenges of classification
👁 20 раз ⏳ 3923 сек.
Лекция 6 - Сhallenges of classification
The Internet is growing at a tremendous rate. The amount of information presented is beyond human comprehen...

🎥 7 - BigData. Formal Context Analysis
👁 15 раз ⏳ 6046 сек.
Лекция 7 - Formal Concept Analysis
В этой лекции рассказывается о том, откуда возник анализ формальных понятий, для чего он используется и какие за...

🎥 8 - BigData. Регрессия
👁 21 раз ⏳ 4118 сек.
Лекция 8 - Регрессия
В лекции рассказана задача регрессии на примере классической задачи предсказания цены дома в Силиконовой Долине. Также рассмот...

🎥 9 - BigData. Хранение и анализ больших данных
👁 22 раз ⏳ 8210 сек.
Лекция 9 - Хранение и анализ больших данных
Лекция дает ответы на такие вопросы как: что такое большие данные, откуда они берутся, как их хранить, ...

🎥 10 - BigData. Deep learning
👁 20 раз ⏳ 5703 сек.
Опубликовано: 19 февр. 2016 г.
Лекция 10 - Deep learning - нейронные сети и их применение.
Лекция рассказывает о истории возникновения и развития н...

​Hey Siri, what does it mean to be human?
Before suggesting that AI can enhance human potential, it can be useful to ask what separates humanity from the progress of technology

🔗 Hey Siri, what does it mean to be human?
Before suggesting that AI can enhance human potential, it can be useful to ask what separates humanity from the progress of technology

​Using MALLET LDA to Learn Why Players Hate Pokémon Sword /Shield

https://towardsdatascience.com/using-mallet-lda-to-learn-why-players-hate-pok%C3%A9mon-sword-shield-23b12e4fc395?source=collection_home---4------1-----------------------

🔗 Using MALLET LDA to Learn Why Players Hate Pokémon Sword /Shield
A simple walk-through on how MALLET LDA can be used to topic model reasons why players didn’t like Pokémon Sword /Shield.

​Predicting Used Car Prices with Machine Learning Techniques

🔗 Predicting Used Car Prices with Machine Learning Techniques
Comparing Performance of Five Different ML Models

Google Cloud Machine Learning -TensorFlow:Methods for Serving TensorFlow Models on GCP|packtpub.com

https://www.youtube.com/watch?v=fXBIq1Dsgqk

🎥 Google Cloud Machine Learning -TensorFlow:Methods for Serving TensorFlow Models on GCP|packtpub.com
👁 1 раз ⏳ 912 сек.
This video tutorial has been taken from Google Cloud Machine Learning with TensorFlow. You can learn more and buy the full video course here https://bit.ly/2tLNY5U

Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

​Most Effective Way To Implement Radial Basis Function Neural Network for Classification Problem

🔗 Most Effective Way To Implement Radial Basis Function Neural Network for Classification Problem
How to use K-Means Clustering along with Linear regression to classify images

​Big Data Analysis for Bioinformatics and Biomedical Discoveries

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Открыть в Telegram



📝 Big Data Analysis for Biomedical Discoveries (en).pdf - 💾6 399 456

​Resolving the Scope of Speculation and Negation using Transformer-Based Architectures

🔗 Resolving the Scope of Speculation and Negation using Transformer-Based Architectures
Speculation is a naturally occurring phenomena in textual data, forming an integral component of many systems, especially in the biomedical information retrieval domain. Previous work addressing cue detection and scope resolution (the two subtasks of speculation detection) have ranged from rule-based systems to deep learning-based approaches. In this paper, we apply three popular

​Fast Neural Network Adaptation via Parameter Remapping and Architecture Search

https://github.com/JaminFong/FNA

Paper: https://arxiv.org/abs/2001.02525v1

🔗 JaminFong/FNA
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search - JaminFong/FNA

​👨‍🦱 DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

Code: https://github.com/EndlessSora/DeeperForensics-1.0

Paper: https://arxiv.org/abs/2001.03024v1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 EndlessSora/DeeperForensics-1.0
Code, model and data of DeeperForensics-1.0 will be made publicly available here. - EndlessSora/DeeperForensics-1.0

Знакомство с машинным обучением на бесплатном интенсиве от Skillbox — отличный шанс начать карьеру в Data Science и стать востребованным специалистом.

Регистрируйся по ссылке: ▶https://clc.to/rxEv9g

Всего три дня занятий — с 13 по 15 января, и ты откроешь себе дверь в профессию будущего!

💡 Интенсив проведёт Михаил Овчинников, главный методист технического направления Skillbox. Вместе с ним ты создашь искусственный интеллект, освоишь Python и Machine Learning с нуля.

​Real or Not? NLP with Disaster Tweets — EDA

🔗 Real or Not? NLP with Disaster Tweets — EDA
Getting started with NLP

​HybridPose: 6D Object Pose Estimation under Hybrid Representations

https://github.com/chensong1995/HybridPose

https://arxiv.org/abs/2001.01869v1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 chensong1995/HybridPose
Implementation of HybridPose: 6D Object Pose Estimation under Hybrid Representation - chensong1995/HybridPose

​What is Stationarity in Time Series and why should you care

🔗 What is Stationarity in Time Series and why should you care
Time Series are Everywhere — Make sure you know how to Handle them

​Accelerate Model Training With Batch Normalization

🔗 Accelerate Model Training With Batch Normalization
What is batch normalization and how does it work? What enables it to achieve faster training? This post aims to answer the above questions

🎥 React with Python Flask and Machine Learning/Vision - THIS IS A TEST!
👁 1 раз ⏳ 5621 сек.
In this video I check out a blog I found where Prediction is used inside a python flask and react app on github, and I also setup a starter github with Python Flask using Visual Studio and React JS as a starting point to do some (non-standard) architectural integration using a module blueprint approach. For this, there will be a second and third video to work through the integration points. Fun hacking on it, it was a litle long, but its cool, the next one will be even better :)

🎥 Machine Learning for Beginners - Supervised vs. Unsupervised Learning
👁 1 раз ⏳ 711 сек.
Welcome back to this series on Machine Learning for Beginners! This video will cover the different types of Machine Learning algorithms - Supervised Learning, Unsupervised Learning, Semisupervised Learning and finally Reinforcement Learning. We're almost to the coding portion - stay tuned!

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THECODEX

​A Practical Guide to Feature Engineering in Python

🔗 A Practical Guide to Feature Engineering in Python
Learn the underlying techniques and tools for effective feature engineering in Python