Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels.
📕 Paper: https://www.mdpi.com/2227-7080/13/3/88
🔥 Dataset: https://www.kaggle.com/code/rinichristy/customer-churn-prediction-2020
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📕 Paper: https://www.mdpi.com/2227-7080/13/3/88
🔥 Dataset: https://www.kaggle.com/code/rinichristy/customer-churn-prediction-2020
@Machine_learn
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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
👍4❤1🥰1
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
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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.
❤7👍1
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
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🖥 Github: https://github.com/reml-group/deliberation-on-priors
📕 Paper: https://arxiv.org/abs/2505.15210v1
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🎓Advanced Applications of Machine Learning in Bioinformatics
🗓Publish year: 2025
📎 Study thesis
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🗓Publish year: 2025
📎 Study thesis
@Machine_learn
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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
🔥4❤2👍1
TabSTAR: A Foundation Tabular Model With
Semantically Target-Aware Representations
📚 Paper
@Machine_learn
Semantically Target-Aware Representations
📚 Paper
@Machine_learn
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COUNTING THE NUMBER OF Zp-AND Fp[t]-FIXED POINTS OF A DISCRETE DYNAMICAL
SYSTEM WITH APPLICATIONS FROM ARITHMETIC STATISTICS
📚 Read
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SYSTEM WITH APPLICATIONS FROM ARITHMETIC STATISTICS
📚 Read
@Machine_learn
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