Projects

### Overview: This project provides a comprehensive guide for deploying deep-learning inference networks and deep vision primitives on NVIDIA Jetson devices using TensorRT and PyTorch. It includes examples for various tasks such as image classification, object detection, semantic segmentation, pose estimation, and action recognition. ### Key Features: - **TensorRT Optimization**: Optimized networks for inference on Jetson devices. - **PyTorch Training**: Training models using PyTorch for transfer learning. - **DNN Primitives**: Support for imageNet, detectNet, segNet, poseNet, and actionNet. - **Realtime Applications**: Examples for streaming from live camera feeds, web apps with WebRTC, and ROS/ROS2 integration. - **Docker Container**: Easy setup using Docker for Jetson environments. - **System Requirements**: Recommended system specifications for optimal performance. ### Tech Stack: - **Language**: C++ - **TensorRT**: Optimized inference library for Jetson devices. - **PyTorch**: Framework for training deep learning models. ### Why It Matters: - **Efficient Inference**: Optimized TensorRT models run faster on Jetson devices. - **Versatile Applications**: Suitable for real-time applications, web development, and robotics. - **Community Support**: Comprehensive documentation and tutorials for Jetson users. ### Repository: - **GitHub Repository**: dusty-nv/jetson-inference - **GitHub Stars**: 90,160 - **Description**: A guide for deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson. ###

### Overview: Transformer Explainer is an interactive visualization tool designed to help anyone learn how Transformer-based models like GPT work. It runs a live GPT-2 model right in your browser, allowing you to experiment with your own text and observe in real time how internal components and operations of the Transformer work together to predict the next tokens. ### Key features: - Real-time interaction with a live GPT-2 model - Experiment with your own text to see how internal components and operations work - Visual explanations of Transformer architecture and operations ### Tech stack: JavaScript ### Why it matters: Transformer Explainer provides a hands-on, interactive way to understand how Transformer-based models like GPT work, making complex concepts accessible and engaging.

Overview: Speech recognition module for Python, supporting several engines and APIs, online and offline. Key features: - Supports CMU Sphinx, Google Speech Recognition, Google Cloud Speech API, Wit.ai, Microsoft Azure Speech, Houndify API, IBM Speech to Text, Snowboy Hotword Detection, Tensorflow, Vosk API, OpenAI whisper, OpenAI Transcription API, Groq Whisper API, and Cohere Transcribe API. Tech stack: Python Why it matters: This library provides a comprehensive solution for speech recognition, making it an essential tool for developers working in various applications such as meeting transcription, voice command recognition, and more.

Overview: 本书是深度学习领域的求职指南，涵盖了数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向的内容。 Key features: - 求职攻略 - 自我介绍 - 数学 - 机器学习 - 深度学习 - 强化学习 - 计算机视觉 - 传统图像处理 - 自然语言处理 - SLAM - 推荐算法 - 数据结构与算法 - 编程语言：C/C++/Python - 深度学习框架 - 面试经验 - 面试技巧 - 其它（计算机网络/Linux等） Tech stack: Python Why it matters: 本书是深度学习领域的求职指南，涵盖了数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向的内容，帮助读者准备面试并提升技能。 Repository: amusi/Deep-Learning-Interview-Book GitHub stars: 8943 GitHub description: 深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向） GitHub URL: https://github.com/amusi/Deep-Learning-Interview-Book

Overview: Rig is a Rust library designed for building scalable and modular LLM-powered applications, featuring agentic workflows, classic agent runtime, and full compatibility with GenAI Semantic Convention. Key features: - Agentic workflows for multi-turn streaming and prompting - Classic agent runtime enabled by default - Full compatibility with GenAI Semantic Convention - Support for 20+ model providers and 10+ vector store integrations - Integration with LLMs with minimal boilerplate - Browser-WASM support for the portable core and classic runtime Tech stack: Rust Why it matters: Rig simplifies the process of integrating LLM-powered applications into projects, providing a modular and scalable solution for developers looking to leverage cutting-edge AI technology.

### Overview: BentoML is a Python library designed to simplify the process of building and deploying online serving systems for AI applications and model inference. It allows developers to easily create APIs for any AI/ML model, manage dependencies and environments, optimize CPU/GPU utilization, and ensure production readiness. ### Key Features: - **Unified Model Serving Framework**: BentoML provides a single, unified framework for building and deploying models and inference APIs. - **Easy Model Integration**: Turn any model inference script into a REST API server with minimal code and standard Python type hints. - **Docker Containers**: Simplifies environment management and dependency setup with a simple config file, generating Docker images and ensuring reproducibility. - **Performance Optimization**: Leverages built-in serving optimization features like dynamic batching, model parallelism, multi-stage pipeline, and multi-model inference-graph orchestration to maximize CPU/GPU utilization. - **Customizability**: Supports any ML framework, modality, and inference runtime, allowing developers to implement their own APIs or task queues with custom logic. - **Production Ready**: Supports local development, running, and debugging, and seamless deployment to production using Docker containers or BentoCloud. ### Tech Stack: Python ### Why It Matters: BentoML simplifies the process of building and deploying AI applications and models, making it easier for developers to integrate and optimize their models for production use. Its unified framework, Docker support, and performance optimization features make it an essential tool for anyone working on AI/ML applications.

### Overview: ImageAI is a Python library designed to empower developers by providing self-contained Computer Vision capabilities, enabling the creation of applications and systems with deep learning and computer vision functionalities. ### Key features: - Supports image prediction and training using 4 different Machine Learning algorithms trained on ImageNet-1000 dataset. - Object detection, video detection, and object tracking using RetinaNet, YOLOv3, and TinyYOLOv3 trained on COCO dataset. - Allows training custom models for detection and recognition of new objects. - Provides support for a wider and more specialized aspects of Computer Vision. ### Tech stack: Python ### Why it matters: ImageAI simplifies the process of integrating advanced Computer Vision capabilities into applications, making it easier for developers to create applications with sophisticated image processing and analysis features.

Overview: Harness SDK is an open-source agent harness and control system designed for building and managing AI agents in Python and TypeScript. It simplifies the process of deploying and managing AI agents without the need for a hosted control plane. Key features: - Python and TypeScript SDKs for agent development - Lifecycle controls, tools, structured output, and multi-agent patterns - Memory management, sessions, model portability, and streaming capabilities - Guardrails, tracing, and evals for safety and observability - Support for various model providers and cloud providers Tech stack: Python and TypeScript Why it matters: Harness SDK simplifies the process of building and managing AI agents, making it easier to deploy and scale AI solutions without the complexity of a hosted control plane.

Overview: This repository contains all the code and software labs for MIT's Introduction to Deep Learning course, designed to be completed at your own pace. Key features: - Lab materials for MIT 6.S191: Introduction to Deep Learning - All lecture slides and videos available online - Google Colaboratory environment for running labs - MITDeepLearning package for convenience functions Tech stack: Jupyter Notebook Why it matters: This repository serves as a comprehensive resource for students and learners interested in deep learning, providing practical labs and lecture materials that can be used both inside and outside the MIT course.

Overview: AiShort is an AI prompt management tool that streamlines the process of creating and using prompts for various AI tools like ChatGPT, Cursor, and others. It offers a vast library of prompts, making it easy to find and use expert-level prompts without the need for extensive training. Key features: - A searchable prompt library - 5000+ ready-to-use AI prompts - Cross-functional prompts for writing, coding, office work, learning, design, marketing, and more - One-click copy functionality - Tag-based and keyword search - Support for 18 languages - Custom prompts and community features Tech stack: TypeScript Why it matters: AiShort simplifies the process of using AI tools by providing a comprehensive library of prompts, making it easier and more efficient to use AI without the need for extensive training or setup.

Overview: Mage OSS is a self-hosted development environment designed for building and running data pipelines locally, offering a fast, visual, and production-ready solution for automating ETL tasks, data flow, and transformations. Key features: - Modular pipelines - Notebook UI - Data integrations - Scheduling - Visual debugging - dbt support Tech stack: Python Why it matters: Mage OSS provides a fast, interactive environment for local pipeline development, making it ideal for teams looking to automate data processing tasks without the need for cloud resources. When ready for enterprise-level tooling and collaboration, Mage Pro offers advanced orchestration, collaboration, and AI-powered workflows.