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《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
A generative world for general-purpose robotics & embodied AI learning.
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
Practical Python Programming (course by @dabeaz)
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
Modeling, training, eval, and inference code for OLMo
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
⚡ TabPFN: Foundation Model for Tabular Data ⚡
A clean and readable Pytorch implementation of CycleGAN
RAGEN leverages reinforcement learning to train LLM reasoning agents in interactive, stochastic environments.
Visualisations of data are at the core of every publication of scientific research results. They have to be as clear as possible to facilitate the communication of research. As data can have differ…
Interpretability for sequence generation models 🐛 🔍
Powerful, open-source AI tools for digital pathology.
Spatiotemporal modeling of spatial transcriptomics
Declarative creation of composable visualization for Python (Complex heatmap, Upset plot, Oncoprint and more~)
HEST: Bringing Spatial Transcriptomics and Histopathology together - NeurIPS 2024 (Spotlight)
A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training.
ML Assistant for Competitive Machine Learning