🦜 Hi!
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://xn--r1a.website/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://xn--r1a.website/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
GitHub
ultimate_posts/where_to_start at master · open-data-science/ultimate_posts
Ultimate posts for opendatascience telegram channel - open-data-science/ultimate_posts
👍56🔥15❤11🥰2😁2🎉2⚡1👎1👏1
Scaling Transformer to 1M tokens and beyond with RMT
Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.
The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m
#deeplearning #nlp #bert #memory
Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.
The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m
#deeplearning #nlp #bert #memory
❤6👍4🤮1