Keras
Showing 36 of 68 projects
A visualizer for neural network, deep learning, and machine learning models across multiple frameworks.
An open standard format for representing machine learning models to enable interoperability between frameworks.
An AutoML library for deep learning that automates model selection and hyperparameter tuning using Keras and TensorFlow.
An open course on reinforcement learning with a practical focus, featuring hands-on labs and comprehensive materials for both online and on-campus students.
An open course on reinforcement learning with a practical focus, featuring hands-on labs and comprehensive materials for both online and on-campus students.
A comprehensive toolset for converting, visualizing, and managing deep learning models across multiple frameworks like TensorFlow, PyTorch, and Caffe.
A Python library implementing state-of-the-art deep reinforcement learning algorithms with seamless Keras integration.
Run trained Keras models directly in the browser with GPU acceleration via WebGL.
A Python module for easily training character- or word-level text-generating neural networks on any dataset with minimal code.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
An easy-to-use, scalable hyperparameter optimization framework for Keras models with define-by-run syntax and built-in search algorithms.
A deep learning project using Keras to build convolutional and recurrent neural networks for high-accuracy captcha recognition.
Deep neural network to extract structured information from invoice documents with a customizable UI and training tools.
A Python library for graph deep learning built on Keras and TensorFlow 2, providing flexible tools for graph neural networks.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A simple wrapper that combines Keras and Hyperopt for convenient hyperparameter optimization in deep learning models.
A TensorFlow library for building, training, and deploying recommender system models with Keras.
A collection of interactive machine learning experiments with Jupyter notebooks for training and browser demos for visualization.
A Python library for automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
A deprecated repository for community-contributed Keras extensions like layers, activations, and loss functions.
Elephas is a Keras extension for distributed deep learning on Apache Spark, enabling data-parallel training at scale.
A TensorFlow library for building Graph Neural Networks with support for heterogeneous graphs and scalable data processing.
A curated collection of open-source computer vision pre-trained models across TensorFlow, Keras, PyTorch, Caffe, and MXNet frameworks.
A lightweight header-only C++ library for running Keras (TensorFlow) models without linking against TensorFlow.
A library of modular computer vision components built on Keras 3, supporting TensorFlow, JAX, and PyTorch backends.
A pretrained modeling library for Keras 3 offering simple, flexible, and fast access to models for text, image, and audio tasks.
GPU-accelerated audio preprocessing layers for Keras/TensorFlow, enabling real-time audio feature extraction within neural networks.
A uniform interface to run deep learning models from multiple frameworks like TensorFlow, PyTorch, and Keras in C++ and Python.
Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.
A CNN-based captcha solver for Taiwan Railway booking website with a training set generator that mimics captcha style and uses data augmentation.
A Python project for algorithmic music generation using recurrent neural networks.
An OpenAI Gym environment for stock market trading simulation with Deep Q-learning and Policy Gradient implementations.
A Siamese neural network for LiDAR-based loop closing and localization by predicting scan overlap and relative yaw angle.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
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