pyhf
pyhf: pure-Python implementation of HistFactory statistical models - Published in JOSS (2021)
Choice-Learn
Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning - Published in JOSS (2024)
PyBCI
PyBCI: A Python Package for Brain-Computer Interface (BCI) Design - Published in JOSS (2023)
deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
datasets
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
zfit
Model manipulation and fitting library based on TensorFlow and optimised for simple and direct manipulation of probability density functions. Its main focus is on scalability, parallelisation and user friendly experience.
quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
decimer
DECIMER Image Transformer is a deep-learning-based tool designed for automated recognition of chemical structure images. Leveraging transformer architectures, the model converts chemical images into SMILES strings, enabling the digitization of chemical data from scanned documents, literature, and patents.
tensorcircuit
Tensor network based quantum software framework for the NISQ era
nobrainer
A framework for developing neural network models for 3D image processing.
tensorcircuit-ng
The next-gen tensor network based quantum software framework: superseding the original TensorCircuit
deepxde
A library for scientific machine learning and physics-informed learning
onnx2tf
Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.
nrel-sup3r
The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
cvnn
Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end
smart-transformers
Smart Transformers are a versatile machine learning tool that can be integrated with Pytorch, TensorFlow, and JAX. Smart transformers provide accurate computations required for cryptographic algorithms. These transformers is that they are independent modules, making it efficient to experiment with various research projects related to cryptanalysis
irl-imitation
Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
emlearn
Machine Learning inference engine for Microcontrollers and Embedded devices
ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform
iree-base-compiler
A retargetable MLIR-based machine learning compiler and runtime toolkit.
orthoseg
OrthoSeg makes it easy to train neural networks to segment orthophotos.
tensorflow-recommenders
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
libtensorflow_cc
Pre-built libtensorflow_cc.so and Docker Images for TensorFlow C++ API
acttensor-tf
ActTensor: Activation Functions for TensorFlow. https://pypi.org/project/ActTensor-tf/ Authors: Pouya Ardehkhani, Pegah Ardehkhani
mosec
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
rapidae
Explore, compare and develop autoencoder models with a back-end agnostic framework
deep-koalarization
Keras/Tensorflow implementation of our paper Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (https://arxiv.org/abs/1712.03400)
pyhpc-benchmarks
A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
tooth-detection-and-numbering
T.C. Maltepe University Graduate Project (Tooth Detection and Numbering From Panoramic Radiography Adult Patients Using Artificial Neural Network)
https://github.com/google-research/retvec
RETVec is an efficient, multilingual, and adversarially-robust text vectorizer.
polyaxon
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
persian-news-crawler
Simple Script To Crawl Data From Persian News Agencies Including Fars, Mehr.
jetson-containers
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
cppe5
Code for our paper CPPE - 5 (Medical Personal Protective Equipment), a new challenging object detection dataset
vehicle_collision_prediction_using_cnn-lstms
Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.
open-rl
Implementations of a large collection of reinforcement learning algorithms.
seglight
Super fast and real-time semantic segmentation (cpu only) can be use for 1 core cpu
mocapnet
We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance
fortran_dnn_from_tf
Barebones method of implementing a pre-trained DNN from TensorFlow in a Fortran script.
transact-tf
An unofficial implementation of "TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest" in Tensorflow
cytologick
(WIP) PyTorch/TensorFlow powered semantic segmentation software for cytological slides
saliency
Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020]
frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models in C++.
music-analysis
Analysis of influence of music using multiple methods, i.e. clustering musical elements, graph centrality of samplings, predicting popularity from lyrics (2018)
style_transfer_gan_project
To ensure a better diagnosis of patients, doctors may need to look at multiple MRI scans. What if only one type of MRI needs to be done and others can be auto-generated? Generative Adversarial Networks (GANs) have been used for generating deepfakes, new fashion styles and high-resolution pictures from the low-resolution ones
transformers-tf-finetune
Scripts to finetune huggingface transformers models with Tensorflow 2
reproducible-research-with-gpu-jupyter
This repository demonstrates how to use GPU-Jupyter for reproducible deep learning research with minimal setup effort..
mobilebruise
Cooking recipes mobile app and REST API built with React Native and Java Script
porkcnn
A Small Project for Pork Barrel Legislation Classification Using Convolutional Neural Networks (Lour's Pork Barrel Classifier (羅老師肉桶法案分類器)🍖🐖 🥩🐷
multimodal-approach-for-ad
Code for "Automated Detection of Alzheimer’s Disease: A Multi-modal Approach With 3D MRI and Amyloid PET" paper
arranger
Official Implementation of "Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music" (ISMIR 2021)
semantic-segmentation-of-landcover.ai-dataset
An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation
air
A deep learning object detector framework written in Python for supporting Land Search and Rescue Missions.
stock-market-prediction-web-app-using-machine-learning-and-sentiment-analysis
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
https://github.com/arya-gaj/your-phone-can-spot-fashion-v1
A lightweight yet powerful system that analyzes short-form videos in real time to identify fashion products by combining computer vision and natural language processing, all processed locally.
xbcr-net
Code for "Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant" (Cell Research)
stochasticfrankwolfe
Implementation of the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.
magic-portrait
Magic Portrait is an android application for transforming the style of a pet in an image to a desired style.
pnif_old
Neural network pruning to reduce the size of Neural Implicit Flow network.
virtual-try-on-trial-clothes
One of the key features of this web application is its virtual try-on feature, which allows users to see how different clothing items would look on them. This feature eliminates the need for users to physically try on clothes in a store, making the shopping experience more convenient and efficient.
videomae
[NeurIPS'22] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
ibm-skills-ai-colab-sessions
PORTFOLIO: IBM Skills Build Programme for Artificial Intelligence - CoLab - Live Sessions & Final Project
ml-optimized-orthogonal-basis-1d-pp
Experimental Python code developed for research on: H. Waclawek and S. Huber, “Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation,” in Learning and Intelligent Optimization, Cham: Springer Nature Switzerland, 2025, pp. 427–441.
videoswin
Keras 3 Implementation of Video Swin Transformers for 3D Video Modeling