Deep Learning
Showing 36 of 937 projects
A simplistic neural network library written in Go with support for single hidden layer and back-propagation learning.
A Ruby library for building and training recurrent neural networks using the TLearn simulator.
A city-scale dataset and platform for learning holistic 3D structures from panoramic and perspective imagery with detailed annotations.
A collection of Jupyter notebooks for learning the neon deep learning framework through hands-on tutorials.
A deep learning system for automatic spoken language identification from audio files using TensorFlow and Caffe.
A Delphi/Pascal binding for TensorFlow and Keras that enables Pascal developers to build, train, and deploy machine learning models.
A PyTorch-based toolbox for graph reliability, focusing on adversarial attacks, defenses, and robustness techniques for graph neural networks.
A TensorLayer re-implementation of CycleGAN with improvements like resize-convolution and instance normalization.
A Clojure library for building and training neural networks with support for various architectures and learning algorithms.
A JAX library for building, training, and evaluating normalizing flows for probabilistic modeling.
A pre-configured Docker image with deep learning frameworks, data science tools, and GPU support for rapid environment setup.
A deep neural network package for Go, designed for simplicity and extensibility.
Python library for audio augmentation, generating multiple audio files from a mono source with speed, tone, and amplitude modifications.
A curated archive of pre-trained computer vision models for object detection, face recognition, fire detection, and more.
A curated collection of books covering Artificial Intelligence, Machine Learning, Deep Learning, and Transformers for students and professionals.
A Go module implementing multi-layer neural networks for machine learning tasks.
A TensorFlow-based model that generates descriptive captions for images using an Inception-v3 encoder and LSTM decoder.
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A pre-release Caffe branch for fully convolutional networks (FCNs), now deprecated with features merged into Caffe master.
A GUI-based tool for training deep neural networks to segment biological images using corrective annotation.
A TensorFlow implementation of Attend, Infer, Repeat (AIR), a generative model for fast scene understanding by reconstructing objects sequentially.
A learning-focused, high-performance tensor computation library built from scratch in Rust with automatic differentiation and CPU/CUDA backends.
A Docker-based speech recognition model that converts short English WAV audio files into text using Mozilla's DeepSpeech.
A neural network model that integrates neighbor information from heterogeneous networks to discover new drug-target interactions.
A modern, fast, and modular deep learning and machine learning framework for Python built on PyTorch.
A multilayer perceptron neural network implementation in Go with backpropagation training.
An implementation of unsupervised image-to-image translation using Generative Adversarial Networks (GANs).
A fungal image classification project using ResNet to identify mushroom species from citizen science and expert sources.
Trains LSTM and CNN models for EEG-based grasp-and-lift detection using Kaggle competition data.
A TensorFlow-based convolutional neural network for recognizing four-digit CAPTCHA images.
A PyTorch deep generative model for integrating and imputing single-cell multimodal data with missing modalities.
Interactive visual pattern search and exploration tool for epigenomic data using unsupervised deep representation learning.
A deep similarity learning-based type inference tool for Python that provides ML-powered type auto-completion.
Code for the Kaggle Dogs vs. Cats image classification competition using deep learning.
An open-source solution for the Airbus Ship Detection Challenge, providing a benchmark and base for ship detection in satellite imagery.
A deep learning algorithm that predicts type hints for Python code using neural networks.
Jupyter notebooks for hands-on Big Data Analytics exercise classes covering Spark ML, Map/Reduce algorithms, and deep learning.
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