Projects sharing features with Foolbox

CAI is a framework for building autonomous security agents and an orchestration system for coordinating multiple specialized agents. It functions as an agentic workflow engine and an autonomous cyber-defense tool that maps language model reasoning to security kill chain functions for threat detection and mitigation. The system distinguishes itself through multi-agent coordination patterns, such as swarms and hierarchies, and the use of stateful conversation handoffs. It implements multi-layer input and output guardrails to block prompt injections and validate commands before they reach the sy

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in

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

Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution. The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex dif

This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs. The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multip

Hyperparameter Experiments with TensorFlow and Keras

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi

This repository contains examples of using Raster Vision on open datasets.

PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model applications. It functions as a generative AI red teaming framework used to simulate adversarial attacks and identify weaknesses in system guardrails. The tool automates AI risk assessment by scanning generative AI components for security vulnerabilities. It utilizes automated testing and analysis to identify security gaps and prevent potential exploits through a consistent, repeatable process. The system incorporates asynchronous model orchestration to compare security postures

Deep learning in Python

Wrapper library for text generation / language models at character and word level with RNNs in TensorFlow

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders

TensorLight - A high-level framework for TensorFlow

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

Caffe2 is a high-performance deep learning framework and C++ machine learning library. It serves as a modular system for designing, training, and executing scalable neural networks. The project functions as an inference engine and a scalable neural network engine designed to run models across distributed systems and diverse hardware. Its architecture allows for the construction of custom neural network components that can be scaled from research to production environments. The framework covers the full lifecycle of deep learning development, including modular network architecture design, mod

"Neural Turing Machine" in Tensorflow

Accelerated deep learning R&D

Deep-pwning is a lightweight framework for experimenting with machine learning models with the goal of evaluating their robustness against a motivated adversary.

ChainerCV: a Library for Deep Learning in Computer Vision

PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da

This project is a deep learning educational resource providing a collection of TensorFlow tutorials and programming exercises. It serves as a set of machine learning code samples designed for university-level courses on machine learning research. The repository focuses on machine learning education and deep learning research, providing practical examples for implementing neural networks from scratch. It supports neural network prototyping and the development of TensorFlow models to help users apply deep learning theory to software implementations.

Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates