Deep Learning
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A curated list of articles covering software engineering best practices for building production machine learning applications.
A curated collection of open-source computer vision pre-trained models across TensorFlow, Keras, PyTorch, Caffe, and MXNet frameworks.
A Pythonic deep learning framework built on NumPy with optional CUDA acceleration.
A deep learning technique for finding semantically-meaningful dense correspondences between images to enable visual attribute transfer.
A modular neural network package for Torch providing building blocks for creating and training deep learning models.
A visual debugger for TensorFlow with breakpoints and real-time data visualization during neural network training.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A curated repository of famous Vision-Language Models (VLMs) detailing their architectures, training procedures, and datasets.
An R interface to TensorFlow, providing access to the complete TensorFlow API for numerical computation and machine learning.
A discontinued Python neural network framework designed for fast, flexible experimentation with CPU and GPU backends.
Train neural networks with OpenStreetMap data and satellite imagery to classify roads and map features.
State-of-the-art pre-trained transformer language models for protein sequences, enabling tasks like structure prediction and function annotation.
A curated collection of resources for deep learning applications in natural language processing.
A minimalist neural network library optimized for sparse data and single-machine environments.
Script to generate question/answer pairs from CNN and Daily Mail articles for machine reading comprehension research.
A PyTorch Lightning project template for structuring deep learning research code to ensure reproducibility and extensibility.
Scene text detection using Connectionist Text Proposal Network (CTPN) for detecting text lines in natural images.
A Python library for 2D/3D object detection and instance segmentation in microscopy images using star-convex shapes.
A deep learning framework for Julia inspired by Caffe, featuring modular architecture and multiple backends.
A Clojure library for neural networks, regression, and feature learning with GPU acceleration support.
A library for building high-performance custom human pose estimation applications with real-time inference and flexible model development.
A curated collection of resources, papers, and frameworks for image-to-image translation research and applications.
A Java deep learning framework implementing neural networks with GPU acceleration via OpenCL and Aparapi.
A high-level builder API for TensorFlow that enables fluent, chainable neural network construction.
A deep learning JavaScript library built from scratch with PyTorch-like syntax and GPU acceleration via GPU.js.
An open-source library providing chest X-ray datasets, pre-trained models, and tools for medical imaging research and analysis.
A Python module for programmatically retrieving NVIDIA GPU status and selecting available GPUs based on memory and load.
A deep learning library for drug-target interaction, drug property, protein-protein interaction, drug-drug interaction, and protein function prediction in bioinformatics.
A Python framework for portfolio optimization using deep learning to allocate investment weights in a single forward pass.
A deep learning system for detecting known objects and estimating their 6-DoF pose from RGB images.
A GPU-accelerated deep learning library for Python using CUDA via PyCUDA, implementing neural networks with various training methods.
A curated list of recent research papers and resources on Vision and Language Pre-trained Models (VL-PTMs).
A method to steer topic and attributes of GPT-2 language models without fine-tuning, enabling controlled text generation.
An autoML framework and toolkit for automating machine learning tasks on graph-structured data.
An open-source study on neural question generation using transformers, providing simplified training and inference pipelines.
A PyTorch implementation for super fast and accurate 3D object detection using LiDAR point clouds, featuring an anchor-free approach.
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