Introduction to self-supervised learning

 

 Indonesia AI Society's Zoominar Daftar di http://bit.ly/IAIS4Nov
Prof. Bambang Riyanto Trilaksono akan memberikan kita introduction to Self-Supervised Learning, salah satu Metoda Deep Learning terbaru, tanggal 4-Nov jam 16:00 agar penggiat AI bisa mengikuti jikalau mendengar presentasi Yann LeCun di tanggal 11-Nov di acara AIS2020. Selain tentunya meningkatkan pengetahuan akan jumlah modelling Deep Learning seperti Reinforcement Learning, Supervised Learning, Unsupervised, etc.., ditambah sekarang: Self-Supervised Learning.. Apakah itu?

CC-100: Monolingual Datasets from Web Crawl Data

 http://data.statmt.org/cc-100/

 

CC-100: Monolingual Datasets from Web Crawl Data

This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.

References

Please cite the following if you found the resources in this corpus useful.

• Unsupervised Cross-lingual Representation Learning at Scale, Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), p. 8440-8451, July 2020, pdf, bib.
• CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data, Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave, Proceedings of the 12th Language Resources and Evaluation Conference (LREC), p. 4003-4012, May 2020, pdf, bib. 

 

cc_net

https://github.com/facebookresearch/cc_net

Tools to download and clean Common Crawl as introduced in our paper CCNet.

 

 

TPC7062Ti

http://www.ybesthope.com/en_product_show.asp?id=37

 Product details：

TPC7062Ti,It's a set of advanced Cortex-A8 CPU as core (frequency 600MHz) high performance embedded integrated touch screen. The design uses 7 inches high brightness TFT LCD display (800 x 480), four wire resistive touch screen (4096 x4096). At the same time also comes loaded with MCGS Embedded Configuration Software (running version), with a strong image display and data processing function.

 

http://www.mcgs.com.cn/

 

TPC7062TX

http://www.mcgs.com.cn/src/products.html?cid=1&id=2 

 

TPC7062TX(KX) MCGS HMI Touch Screen 7 inch 800*480 with program cable NE

https://boardss.de/servoantriebe-c-17/tpc7062txkx-mcgs-hmi-touch-screen-7-inch-800480-with-program-cable-ne-p-3775.html?language=En 

TPC7062TX MPU618-37-V1.2 CA-F121 E198681 PC070TN98液晶屏

https://world.taobao.com/item/619095213617.htm

 

 

Data Science in Education Using R

 https://twitter.com/kierisi/status/1319276121515384834

 https://datascienceineducation.com/

Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez

Welcome

Welcome to Data Science in Education Using R! Inspired by {bookdown}, this book is open source. Its contents are reproducible and publicly accessible for people worldwide. The online version of the book is hosted at datascienceineducation.com.