pytorch-A3C
MorvanZhou
Simple A3C implementation with pytorch + multiprocessing
모델 학습, 평가, 추론 프레임워크를 찾고 커뮤니티의 관심도를 비교하세요.
MorvanZhou
Simple A3C implementation with pytorch + multiprocessing
moshafieeha
A curated list of valuable resources from our studies at the University of Tehran (UT), School of Electrical and Computer Engineering (ECE)
Mostafa-Samir
A TensorFlow implementation of DeepMind's Differential Neural Computers (DNC)
motefly
SIGKDD'2019: DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks
motiwari
BanditPAM C++ implementation and Python package
movehand
A Python implementation of RAISR
mozilla
Examples of how to use or integrate DeepSpeech
mpaepper
Build a chatbot or Q&A bot of your website's content
mrdbourke
Code and files to go along with CS329s machine learning model deployment tutorial.
mrdbourke
Code for testing various M1 Chip benchmarks with TensorFlow.
MrGiovanni
[MICCAI 2019 Young Scientist Award] [MedIA Best Paper Award] Models Genesis: self-supervised pre-training for 3D medical images. Learns transferable representations from unlabeled CT and MRI volumes, then fine-tunes for downstream segmentation and classification. Keras and PyTorch weights included.
msg-systems
msnh2012
🔥 (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.
msracver
The source code of "Deep Exemplar-based Colorization".
msu-video-group
[ECCV'26] FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation
msurtsukov
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
Mubelotix
muellerberndt
muhanzhang
SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NeurIPS 2018 spotlight".
MuQiuJun-AI
超轻量级bert的pytorch版本,大量中文注释,容易修改结构,持续更新
murari023
A curated list of background subtraction related papers and resources
Murgio
food image to recipe with deep convolutional neural networks.
Murrough-Foley
Fast, accurate web content extraction in Rust. ML page-type classification, per-type extraction, confidence scoring. F1=0.966 on ScrapingHub (#1), F1=0.859 across 2,008 annotated pages (1,497 development + 511 held-out test