deep-learning

Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch

Descriptive Deep Learning

List of Deep Learning Cloud Providers

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

code for our ICCV 2021 paper "DeepCAD: A Deep Generative Network for Computer-Aided Design Models"

[CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image

Deep learning with spiking neural networks (SNNs) in PyTorch.

A CLI for Kubeflow.

Papers about explainability of GNNs

[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.

High-performance Vision library in Python. Scale your research, not boilerplate.

A header-only C++ library for numerical optimization --

depyf is a tool to help you understand and adapt to PyTorch compiler torch.compile.

From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :)

Training/Fine-tuning at the speed of light

Implementation of the sparse attention pattern proposed by the Deepseek team in their "Native Sparse Attention" paper

NMA deep learning course

Templating language written for Stable Diffusion workflows. Available as an extension for the Automatic1111 WebUI.

[TMLR 2025🔥] A survey for the autoregressive models in vision.

A powerful baseline for image classification, face recognition and image retrieval with Pytorch

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

PhoBERT: Pre-trained language models for Vietnamese (EMNLP-2020 Findings)

A curated list of awesome Active Learning

Resources about time series forecasting and deep learning.

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

Train Models Contrastively in Pytorch

DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning

A comprehensive book on neural networks and large language models in NLP

A Python high-performance screen capture library for Windows using Desktop Duplication API - Updated 2026

A fast and differentiable QP solver for PyTorch.