ML Research Hub – Telegram
ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

🖥 Github: https://github.com/dvlab-research/longlora

📕 Paper: https://arxiv.org/pdf/2309.12307v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pg-19

https://news.1rj.ru/str/DataScienceT
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🎓 BayesDLL: Bayesian Deep Learning Library

New Bayesian neural network library for PyTorch for large-scale deep network

🖥 Github: https://github.com/samsunglabs/bayesdll

📕 Paper: https://arxiv.org/abs/2309.12928v1

⭐️ Dataset: https://paperswithcode.com/dataset/oxford-102-flower

https://news.1rj.ru/str/DataScienceT
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fastMONAI: A low-code deep learning library for medical image analysis

Simplifying deep learning for medical imaging.

git clone https://github.com/MMIV-ML/fastMONAI

🖥 Github: https://github.com/MMIV-ML/fastMONAI

Project: https://fastmonai.no

📕 Paper: https://www.sciencedirect.com/science/article/pii/S2665963823001203

🖥 Colab: https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/master/nbs/10a_tutorial_classification.ipynb

https://news.1rj.ru/str/DataScienceT
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⭐️Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation

git clone https://github.com/Yuxinn-J/Scenimefy.git

🖥 Github: https://github.com/Yuxinn-J/Scenimefy/tree/main

☑️ Demo: https://huggingface.co/spaces/YuxinJ/Scenimefy

📕 Paper: https://arxiv.org/abs/2308.12968

Project: https://yuxinn-j.github.io/projects/Scenimefy.html

⭐️ Dataset: https://github.com/Yuxinn-J/Scenimefy/tree/main#open_file_folder-anime-scene-dataset

https://news.1rj.ru/str/DataScienceT
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🤖 Machine Learning Tutorials Repository

1. Python
2
. Computer Vision: Techniques, algorithms
3
. NLP
4.
Matplotlib
5. NumPy
6. Pandas
7.
MLOps
8. LLMs
9.
PyTorch/TensorFlow

git clone https://github.com/patchy631/machine-learning

🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main

⭐️ https://news.1rj.ru/str/DataScienceT
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⭐️ Decaf: 3D Face-Hand Interactions

⭐️ The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos

🔗 Review: https://t.ly/070Tj

🔗 Paper: arxiv.org/pdf/2309.16670.pdf

🔗 Project: vcai.mpi-inf.mpg.de/projects/Decaf

https://news.1rj.ru/str/DataScienceT
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🔊 Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

git clone git@github.com:guyyariv/TempoTokens.git

🖥 Github: https://github.com/guyyariv/TempoTokens

📕 Paper: https://arxiv.org/abs/2309.16429v1

⭐️ Dataset: https://paperswithcode.com/dataset/audioset

https://news.1rj.ru/str/DataScienceT
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☑️ IBM Free course: Python Basics for Data Science

Course Link: https://www.edx.org/learn/python/ibm-python-basics-for-data-science

⭐️ https://news.1rj.ru/str/CodeProgrammer
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🔊 Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

git clone git@github.com:guyyariv/TempoTokens.git

🖥 Github: https://github.com/guyyariv/TempoTokens

📕 Paper: https://arxiv.org/abs/2309.16429v1

⭐️ Dataset: https://paperswithcode.com/dataset/audioset

https://news.1rj.ru/str/DataScienceT
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✅️ T3Bench: Benchmarking Current Progress in Text-to-3D Generation

🖥 Github: https://github.com/THU-LYJ-Lab/T3Bench

📕 Paper: https://arxiv.org/abs/2310.02977v1

⭐️ Dataset: https://paperswithcode.com/dataset/nerf

https://news.1rj.ru/str/DataScienceT
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