NVIDIA just open sourced Open Code Reasoning models - 32B, 14B AND 7B - APACHE 2.0 licensed π₯
> Beats O3 mini & O1 (low) on LiveCodeBench π
Backed by OCR dataset the models are 30% token efficient than other equivalent Reasoning models
Works with llama.cpp, vLLM, transformers, TGI and more - check them out today!!
https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B
@Machine_learn
> Beats O3 mini & O1 (low) on LiveCodeBench π
Backed by OCR dataset the models are 30% token efficient than other equivalent Reasoning models
Works with llama.cpp, vLLM, transformers, TGI and more - check them out today!!
https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B
@Machine_learn
A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection
π Paper: https://www.mdpi.com/2227-7080/13/2/53
π₯ Datasets:
KTH: https://www.csc.kth.se/cvap/actions/
UCF Sports: https://www.crcv.ucf.edu/research/data-sets/ucf-sports-action/
HMDB51: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
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π Paper: https://www.mdpi.com/2227-7080/13/2/53
π₯ Datasets:
KTH: https://www.csc.kth.se/cvap/actions/
UCF Sports: https://www.crcv.ucf.edu/research/data-sets/ucf-sports-action/
HMDB51: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
@Machine_learn
Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels.
π Paper: https://www.mdpi.com/2227-7080/13/3/88
π₯ Dataset: https://www.kaggle.com/code/rinichristy/customer-churn-prediction-2020
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π Paper: https://www.mdpi.com/2227-7080/13/3/88
π₯ Dataset: https://www.kaggle.com/code/rinichristy/customer-churn-prediction-2020
@Machine_learn
Exercises in Machine Learning
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
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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 βοΈ: 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 βοΈ: 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
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@Machine_learn
Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
π₯ Github: https://github.com/reml-group/deliberation-on-priors
π Paper: https://arxiv.org/abs/2505.15210v1
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π₯ Github: https://github.com/reml-group/deliberation-on-priors
π Paper: https://arxiv.org/abs/2505.15210v1
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πAdvanced Applications of Machine Learning in Bioinformatics
πPublish year: 2025
π Study thesis
@Machine_learn
πPublish year: 2025
π Study thesis
@Machine_learn
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
24 Apr 2025 Β· Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang Β·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
24 Apr 2025 Β· Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang Β·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
TabSTAR: A Foundation Tabular Model With
Semantically Target-Aware Representations
π Paper
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Semantically Target-Aware Representations
π Paper
@Machine_learn