Telegram Web Link
Machine learning books and papers pinned «با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد 1: introduction to machine learning 2: Regression (linear and non-linear) 3: Tensorflow…»
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion

🖥 Github: https://github.com/haha-lisa/creativesynth

📕 Paper: https://arxiv.org/pdf/2401.14066v1.pdf

Tasks: https://paperswithcode.com/task/image-generation

@Machine_learn
This media is not supported in your browser
VIEW IN TELEGRAM
🌠AnyDoor: Zero-shot Object-level Image Customization

pip install git+https://github.com/cocodataset/panopticapi.git

pip install pycocotools -i https://pypi.douban.com/simple

pip install lvis


🖥 Code: https://github.com/damo-vilab/AnyDoor

🎓 HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online

🔮 Project Page: https://damo-vilab.github.io/AnyDoor-Page/

📚 ArXiv: https://arxiv.org/abs/2307.09481

@Machine_learn
OReilly.Training.Data.for.Machine.Learning.pdf
21.3 MB
Book: 📚Training Data for Machine Learning: Human Supervision from Annotation to Data Science (2023)
Authors: Anthony Sarkis
ISBN: null
year: 2023
pages: 332
Tags: #Machine_learning#Data
@Machine_learn
💊 AMIE: A research AI system for diagnostic medical reasoning and conversations

💡 Blog: https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html

📚 Paper: https://arxiv.org/abs/2401.05654

@Machine_learn
TimesFM is a forecasting model, pre-trained on a large time-series corpus of 100 billion real world time-points

https://blog.research.google/2024/02/a-decoder-only-foundation-model-for.html

@Machine_learn
📷 InstructIR: High-Quality Image Restoration Following Human Instructions


🖥 Code: https://github.com/mv-lab/InstructIR

🚀 Project: mv-lab.github.io/InstructIR/

🎮 Colab: https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq

📚 Paper: https://arxiv.org/abs/2401.16468

@Machine_learn
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels

🖥 Github: https://github.com/IIPL-CAU/SoftEDA

📕 Paper: https://arxiv.org/pdf/2402.05591v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/cola

@Machine_learn
2025/07/06 09:30:38
Back to Top
HTML Embed Code: