Exercises in Machine Learning
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
@Machine_learn
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
@Machine_learn
DeepSeek-Coder
DeepSeek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and an extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, DeepSeek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
Creator: Deepseek-AI
Stars ⭐️: 15.6k
Forked by: 1.5k
Github Repo:
https://github.com/deepseek-ai/DeepSeek-Coder
@Machine_learn
DeepSeek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and an extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, DeepSeek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
Creator: Deepseek-AI
Stars ⭐️: 15.6k
Forked by: 1.5k
Github Repo:
https://github.com/deepseek-ai/DeepSeek-Coder
@Machine_learn
GitHub
GitHub - deepseek-ai/DeepSeek-Coder: DeepSeek Coder: Let the Code Write Itself
DeepSeek Coder: Let the Code Write Itself. Contribute to deepseek-ai/DeepSeek-Coder development by creating an account on GitHub.
probability_cheatsheet.pdf
789.3 KB
Probability Cheatsheet
@Machine_learn
@Machine_learn
Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
🖥 Github: https://github.com/reml-group/deliberation-on-priors
📕 Paper: https://arxiv.org/abs/2505.15210v1
@Machine_learn
🖥 Github: https://github.com/reml-group/deliberation-on-priors
📕 Paper: https://arxiv.org/abs/2505.15210v1
@Machine_learn
🎓Advanced Applications of Machine Learning in Bioinformatics
🗓Publish year: 2025
📎 Study thesis
@Machine_learn
🗓Publish year: 2025
📎 Study thesis
@Machine_learn
🤼 مسابقه برنامه‌نویسی کوئرا در حوزه هوش‌مصنوعی با همکاری فدراسیون کشتی ایران
🎁 بیش از ۶۰ میلیون تومان جایزه نقدی و کلی هدایای جذاب دیگه…
✔️ فرصت استخدام
✔️ گواهینامه معتبر
✔️ فینال حضوری جذاب در کنار بزرگان کشتی ایران!
🌐 اطلاعات بیشتر و ثبت‌نام رایگان:
🔗 https://quera.org/r/qpey6
➖➖➖➖
#Quera
🎁 بیش از ۶۰ میلیون تومان جایزه نقدی و کلی هدایای جذاب دیگه…
✔️ فرصت استخدام
✔️ گواهینامه معتبر
✔️ فینال حضوری جذاب در کنار بزرگان کشتی ایران!
🌐 اطلاعات بیشتر و ثبت‌نام رایگان:
🔗 https://quera.org/r/qpey6
➖➖➖➖
#Quera
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
24 Apr 2025 · Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang ·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
24 Apr 2025 · Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang ·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
Forwarded from Paper cite
با عرض سلام
براي ٣ مقاله در مرحله سابميت مي تونيم سايت بزنيم.
Image classification
NLP
Stock market
@Raminmousa
@Machine_learn
https://yangx.top/papercite
براي ٣ مقاله در مرحله سابميت مي تونيم سايت بزنيم.
Image classification
NLP
Stock market
@Raminmousa
@Machine_learn
https://yangx.top/papercite
Telegram
Paper cite
ارسال مقاله جهت سايت
@Raminmousa
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هدف اين كانال حل مشكل سايت زني به مقالات. از طرفي كساني كه نيازمند هزينه سايت هستن نيز مي تونن با سايت زدن به هر مقاله ي اين كانال بخشي از هزينه رو دريافت كنن.
@Raminmousa
------
هدف اين كانال حل مشكل سايت زني به مقالات. از طرفي كساني كه نيازمند هزينه سايت هستن نيز مي تونن با سايت زدن به هر مقاله ي اين كانال بخشي از هزينه رو دريافت كنن.
TabSTAR: A Foundation Tabular Model With
Semantically Target-Aware Representations
📚 Paper
@Machine_learn
Semantically Target-Aware Representations
📚 Paper
@Machine_learn