tqdm
tqdm: A Fast, Extensible Progress Meter for Python and CLI - Published in JOSS (2019)
Foolbox Native
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX - Published in JOSS (2020)
Larq
Larq: An Open-Source Library for Training Binarized Neural Networks - Published in JOSS (2020)
UnlockNN
UnlockNN: Uncertainty quantification for neural network models of chemical systems - Published in JOSS (2022)
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.
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
deep-koalarization
Keras/Tensorflow implementation of our paper Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (https://arxiv.org/abs/1712.03400)
uncertainty-wizard
Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.
polyaxon
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
pyscipopt-ml
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
https://github.com/mikekeith52/scalecast
The practitioner's forecasting library
hybrid-vocal-classifier
a Python machine learning library for animal vocalizations and bioacoustics
ktrain
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
open-rl
Implementations of a large collection of reinforcement learning algorithms.
face-mask-detection
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
netron
Visualizer for neural network, deep learning and machine learning models
py-torchsummary
Model summary in PyTorch similar to `model.summary()` in Keras
emotion-recognition-using-speech
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
geoconv
A Python library for end-to-end learning on surfaces. It implements pre-processing functions that include geodesic algorithms, neural network layers that operate on surfaces, visualization tools and benchmarking functionalities.
sinfnet
A collection of datasets and neural networks for microorganism image classification
https://github.com/ari-dasci/s-tsfe-dl
Time Series Feature Extraction using Deep Learning
t81_558_deep_learning
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
keras-gpt-copilot
Integrate an LLM copilot within your Keras model development workflow
https://github.com/bagustris/dl_mir_tutorial2
Tutorial Deep Learning dengan Keras berbasis Theano untuk pengenalan genre musik
https://github.com/bryanbocao/incompletecode
Self-contained Reinforcement Learning Algorithms.
mantis-ml
mantis-ml: Stochastic semi-supervised learning to prioritise genes from high throughput genomic screens
https://github.com/brainglobe/cellfinder-napari
Efficient cell detection in large images using cellfinder in napari
zookeeper
A small library for managing deep learning models, hyperparameters and datasets
https://github.com/awslabs/keras-apache-mxnet
[DEPRECATED] Amazon Deep Learning's Keras with Apache MXNet support
hyperas
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
https://github.com/beomi/tf-keras-on-lambda
Example Repo for Tensorflow + Keras on AWS Lambda
https://github.com/broadinstitute/keras-resnet
Keras package for deep residual networks
https://github.com/bytedance/byteps
A high performance and generic framework for distributed DNN training
https://github.com/carlomazzaferro/kryptoflow
Real-time analysis of bitcoin markets with Kafka and Tensorflow Serving
https://github.com/bpesquet/mlkatas
(Phased out) A series of challenges for practicing your Machine Learning and Deep Learning skills
https://github.com/szymonmaszke/torchlayers
Shape and dimension inference (Keras-like) for PyTorch layers and neural networks
https://github.com/marcelwinterot/nano-keras
Deep learning library inspired by Keras
Photovoltaic_Fault_Detector
Model Photovoltaic Fault Detector based in model detector YOLOv.3, this repository contains four detector model with their weights and the explanation of how to use these models.
https://github.com/cambridge-iccs/fortran-tf-lib
A library for directly calling TensorFlow / Keras ML models from Fortran.
https://github.com/ahmedshahriar/deeplearning.ai-tensorflow-developer-professional-certificate
My notes and assignments on DeepLearning.AI TensorFlow Developer Professional Certificate
https://github.com/aryashah2k/handwritten-multiple-digits-recognizer
An Intuitive Desktop GUI Application For Recognizing Multiple Handwritten Digits Drawn At The Same Time. Trained On MNIST Dataset and Built With Python, OpenCV and TKinter
https://github.com/ahmedshahriar/housing-price-prediction
Data science project on Housing Prices Dataset regression analysis
https://github.com/alicerunsonfedora/abysima
A machine learning experiment with generating languages.
https://github.com/aalling93/ecnn-on-sar-data-and-radiometry-data
Deep learning for Synthetic Aperture Radar(SAR) and Radiometry data. An Ensemble Convolutional Neural Network workflow is implemented with data acquisition, processing, labelling, creating model, training model and launching a model
https://github.com/cedrickchee/data-science-notebooks
Data science Python notebooks—a collection of Jupyter notebooks on machine learning, deep learning, statistical inference, data analysis and visualization.
trainyourownyolo
Train a state-of-the-art yolov3 object detector from scratch!
https://github.com/dineshpinto/ml-droplet-recognition
Neural network for micro-fluidic droplet LLPS recognition
frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models in C++.
https://github.com/amr-yasser226/deep-learning-journal
A personal collection of Jupyter notebooks, scripts, and resources documenting my exploration and learning in Deep Learning. From foundational neural network concepts to advanced transformer architectures, this repo tracks experiments, notes, and implementations.
multimodal-approach-for-ad
Code for "Automated Detection of Alzheimer’s Disease: A Multi-modal Approach With 3D MRI and Amyloid PET" paper
listening-beyond-the-labels-v1
A scalable and non-invasive speech-based machine learning model for early Alzheimer's detection using mel-spectrograms and lightweight semi-supervised CNN with no transcription or neuroimaging needed.
https://github.com/cemenenkoff/qsnake
Explore how to use a deep Q-learning network to train an agent to play the classic game of Snake.
https://github.com/alexeyev/keras-generating-sentences-from-a-continuous-space
Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349
https://github.com/atharvapathak/melanoma_detection_case_study
Creating a possible model to detect melanoma from the dataset accurately using CNN.
https://github.com/beringresearch/lrfinder
Learning Rate Finder using Tensorflow Dataset
https://github.com/chris10m/rfb-text-detection
A Dense Text Detection model using Receptive Field Blocks
medical-analysis-assistant
a web appplication to assist with heart disease prediction, skin cancer and tubercolosis detection also with a health chatbot.
multi-shared-task-self-supervised-cnn-lstm
Multi-shared-task Self-supervised Learning utilizing CNN-LSTM network
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
maltese-christian-statue-classifier
An AI initiative project, the Maltese Christian Statue (MCS) Classifier preserves and celebrates Maltese religious culture by accurately classifying 17 distinct categories of Christian statues, fostering deeper understanding and appreciation for the Maltese Culture.
https://github.com/ahmedshahriar/time-series-projects
Collection of my Time series Analysis Projects
iust_deep_fuzz
Advanced file format fuzzer based-on deep neural language models.
https://github.com/materialsvirtuallab/megnet
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
ibm-skills-ai-colab-sessions
PORTFOLIO: IBM Skills Build Programme for Artificial Intelligence - CoLab - Live Sessions & Final Project
air
A deep learning object detector framework written in Python for supporting Land Search and Rescue Missions.
https://github.com/alamkanak/parkinsons-research
An experiment to detect Parkinson's Disease using speech data
mobilebruise
Cooking recipes mobile app and REST API built with React Native and Java Script
deakin-ai-challenge2021
The Deakin Simpsons challenge 2021 is a computer vision competition for which the goal is to recognize Simpsons characters individually in images using machine learning/deep learning. The challenge is designed to provide students with the opportunity to work as team members, to compete with each other, and to enhance the student learning experience by improving their AI modeling, problem-solving, and team-working skills.
sarlvision
A reinforcement learning object detector leveraging saliency ranking, offering a self-explainable system with a fully observable action log. | B.Sc. IT (Hons) Artificial Intelligence Dissertation | UOM Dean's List Awards 2024
videoswin
Keras 3 Implementation of Video Swin Transformers for 3D Video Modeling
videomae
[NeurIPS'22] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/aidinhamedi/pneumonia-detection-ai
This project uses a deep learning model built with the TensorFlow Library to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model.