Projects sharing features with Pytorch DPPO

Rainbow: Combining Improvements in Deep Reinforcement Learning

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

Status: Archive (code is provided as-is, no updates expected)

Accelerated RL

Emergent Translation in Multi-Agent Communication PyTorch implementation of the models described in the paper Emergent Translation in Multi-Agent Communication.

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b

⚡️A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion 🐍

Search and filter videos based on objects that appear in them using convolutional neural networks

This project is a TensorFlow-based neural style transfer tool and deep learning image processor. It uses convolutional neural networks to apply the artistic style of one image to the content of another through neural image synthesis. The system supports multi-style blending to combine artistic characteristics from several different images into a single output. It also includes color-preserving stylization, which maintains the original color palette of the source image by merging source color data with the luminance of the stylized result. The tool provides capabilities for style abstraction

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra