Repository History
50 repositories tagged with Deep Learning
GLM-OCR: Accurate, Fast, and Comprehensive Multimodal OCR Model
GLM-OCR is a powerful multimodal OCR model designed for complex document understanding, built on the GLM-V encoder-decoder architecture. It achieves state-of-the-art performance across various benchmarks, offering efficient inference and easy integration. This open-source solution is optimized for real-world business scenarios, providing robust and high-quality OCR capabilities.
AI Engineering from Scratch: A Comprehensive Hands-On AI Curriculum
The "AI Engineering from Scratch" repository provides a free, MIT-licensed curriculum for mastering AI engineering from foundational math to advanced agent systems. It emphasizes a hands-on approach, guiding learners to build every algorithm from scratch before utilizing frameworks. With 435 lessons across 20 phases, this project equips students with the practical skills needed to professionally build and deploy AI solutions.
Qwen3: Alibaba Cloud's Advanced Large Language Model Series
Qwen3 is a powerful series of large language models developed by the Qwen team at Alibaba Cloud. It offers advanced capabilities in reasoning, multilingual support, and long-context understanding, available in various sizes and modes for diverse applications. This repository provides comprehensive resources for running, deploying, and building with Qwen3 models.
AI-Scientist-v2: Automated Scientific Discovery via Agentic Tree Search
AI-Scientist-v2 is an advanced agentic system designed for automated scientific discovery, capable of generating hypotheses, running experiments, analyzing data, and writing scientific manuscripts. This system has successfully produced the first workshop paper written entirely by AI and accepted through peer review, marking a significant step towards fully autonomous research.
PyTorch Image Models (timm): The Ultimate Collection of Image Encoders
PyTorch Image Models (timm) is an extensive library offering the largest collection of PyTorch image encoders and backbones. It provides a wide array of state-of-the-art models, complete with pretrained weights, training, evaluation, and inference scripts. This makes it an invaluable resource for researchers and developers working with computer vision tasks in PyTorch.
Kapre: Keras Audio Preprocessors for Real-time GPU Processing
Kapre is a powerful Python library that provides Keras layers for real-time audio preprocessing directly on GPUs. It enables efficient computation of STFT, Melspectrograms, and other audio features within your deep learning models. This integration simplifies model deployment, allows for DSP parameter optimization, and ensures consistency compared to traditional pre-computation or custom implementations.
Kimi-k1.5: Scaling Reinforcement Learning with LLMs and Multimodality
Kimi-k1.5 introduces an o1-level multi-modal model that significantly advances reinforcement learning with Large Language Models. It demonstrates state-of-the-art performance in short-CoT tasks, outperforming leading models like GPT-4o and Claude Sonnet 3.5, and matches o1 performance in long-CoT scenarios across various modalities. This project highlights key innovations in long context scaling and improved policy optimization.
CoTracker: A Powerful Model for Tracking Any Point on a Video
CoTracker is a state-of-the-art model developed by Facebook AI Research and the University of Oxford, designed for tracking any point (pixel) across video sequences. This transformer-based solution offers fast, accurate, and quasi-dense point tracking capabilities. It is an invaluable tool for researchers and developers in computer vision, enabling precise analysis of motion in videos.
Spark-TTS: Efficient LLM-Based Text-to-Speech with Zero-Shot Voice Cloning
Spark-TTS is an advanced text-to-speech system that leverages large language models (LLM) for highly accurate and natural-sounding voice synthesis. Built on Qwen2.5, it offers streamlined efficiency, high-quality zero-shot voice cloning, bilingual support for Chinese and English, and controllable speech generation, making it versatile for both research and production.
AudioSep: Foundation Model for Open-Domain Sound Separation with Language Queries
AudioSep is a groundbreaking foundation model for open-domain sound separation, allowing users to isolate specific sounds using natural language descriptions. It demonstrates strong performance and impressive zero-shot generalization across various tasks, including audio event, musical instrument, and speech separation. This powerful tool simplifies complex audio processing with intuitive text-based queries.
JAX: Composable Transformations for Python+NumPy Programs
JAX is a powerful Python library designed for high-performance numerical computing and large-scale machine learning. It offers composable function transformations like automatic differentiation, JIT compilation to accelerators (GPU/TPU), and auto-vectorization. This powerful combination allows developers to write flexible and efficient numerical programs.
Translation Agent: Agentic Translation with LLM Reflection Workflow
Translation Agent is a Python demonstration of an agentic workflow for machine translation, leveraging large language models (LLMs) and a reflection process. This innovative approach aims to improve translation quality by having the LLM translate, reflect on its output, and then refine the translation based on its own suggestions. It offers significant customizability for style, idioms, and regional language variations, making it a promising direction for future translation technologies.