Projects sharing features with Nn Theano

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The

A GPU (CUDA) based Artificial Neural Network library

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a

Reimplementation Antol et al 2015 Keras-based LSTM/CNN models for Visual Question Answering

Library for automatic retraining and continual learning

A lightweight header-only library for using Keras (TensorFlow) models in C++.

Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

:grey_question: Visual Question Answering in Torch

ECCV 2020 PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

PyTorch-style and human-readable RegNet with a spectrum of pre-trained models

Please use, cite, and contribute to PlantCV! If you have questions, please submit them via the GitHub issues page. Follow us on twitter @plantcv.

Code for Kaggle EEG Detection competition

Language Model GRU with Python and Theano

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

Powerful Powerful Machine Learning library with GPU, CPU and WASM backends

simple neural network library in ANSI C

This repository reproduces the results of the paper: "Fixing the train-test resolution discrepancy" https://arxiv.org/abs/1906.06423

Codebase for Image Classification Research, written in PyTorch.

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in

Behavior Tree Starter Kit