xiang's repositories
brpc
Industrial-grade RPC framework used throughout Baidu, with 600,000+ instances and 500+ kinds of services, called "baidu-rpc" inside Baidu.
CV-CUDA
CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.
faiss
A library for efficient similarity search and clustering of dense vectors.
FastDeploy
⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
Paddle
PArallel Distributed Deep LEarning
tools
小工具集
apollo
An open autonomous driving platform
awesome
:sunglasses: Curated list of awesome lists
blog
博客
caffe
Caffe: a fast open framework for deep learning.
cdp
Code for our ECCV 2018 work.
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Embedded-Systems-Exam
Implementation of One Sided Jacobi SVD using CUDA on Jetson TK1 embedded GPU
img2img-turbo
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
leetcode
LeetCode Problems' Solutions
llm.c
LLM training in simple, raw C/CUDA
milvus
An open-source vector database for embedding similarity search and AI applications.
numpy-ml
Machine learning, in numpy
paddle-mobile
This research aims at simply deploying deeplearning on mobile and embedded devices, with low complexity and high speed. old name mobile deep learning.
PhotographicImageSynthesis
Photographic Image Synthesis with Cascaded Refinement Networks
practicalAI
📚A practical approach to learning and using machine learning.
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
scikit-learn
scikit-learn: machine learning in Python
Spring-Boot-Reference-Guide
Spring Boot Reference Guide中文翻译 -《Spring Boot参考指南》
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
TinyLlama
The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
VisualDL
A platform to visualize the deep learning process.
yii2-redis
Yii 2 Redis extension.