Pinned Repositories
faceFrontalization
Align a face in profile to front view
camshiftKalman
An object tracking project using camshift and Kalman Filter based on OpenCV
DeepNet
I implement RBM, DBN, Multi-modal DBN with Python and the majority of matrix operations are executed on GPU. In this project I employ Cudamat and Numpy python libs.
cdbn_matlab
an implementation of Convolutional Deep Belief Network (CDBN) using Matlab
WideDeepLearning
A general implementation of linear/dnn/wide&deep learning model
CDBN
An implementation of Convolution Deep Belief Network (CDBN) using C++
ardroneToolkit
A toolkit for one quradrotor named ardrone
Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
CUDA
GPU-accelerated LIBSVM is a modification of the original LIBSVM that exploits the CUDA framework to significantly reduce processing time while producing identical results. The functionality and interface of LIBSVM remains the same. The modifications were done in the kernel computation, that is now performed using the GPU.
DIG
A toolkit for data mining and machine learning
shaoguangcheng's Repositories
shaoguangcheng/Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
shaoguangcheng/diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
shaoguangcheng/Anything-3D
Segment-Anything + 3D. Let's lift anything to 3D.
shaoguangcheng/safe-mbrl
Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method
shaoguangcheng/ColossalAI
Making large AI models cheaper, faster and more accessible
shaoguangcheng/ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
shaoguangcheng/Safe-RL
shaoguangcheng/easy-rl
强化学习中文教程,在线阅读地址:https://datawhalechina.github.io/easy-rl/
shaoguangcheng/RL-Paper-notes
shaoguangcheng/estool
Evolution Strategies Tool
shaoguangcheng/reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
shaoguangcheng/DIM
Deep InfoMax (DIM), or "Learning Deep Representations by Mutual Information Estimation and Maximization"
shaoguangcheng/Text_Classification
Text Classification Algorithms: A Survey
shaoguangcheng/shaoguangcheng.github.io
shaoguangcheng/attention
some attention implements
shaoguangcheng/euler
A distributed graph deep learning framework.
shaoguangcheng/Sequential_Recommendation
shaoguangcheng/bert
TensorFlow code and pre-trained models for BERT
shaoguangcheng/AutoInt
Implementation of AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
shaoguangcheng/GAT
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
shaoguangcheng/DeepInterestNetwork
shaoguangcheng/dien
shaoguangcheng/GraphSAGE
Representation learning on large graphs using stochastic graph convolutions.
shaoguangcheng/xDeepFM
shaoguangcheng/recsys2018-evaluation-tutorial
shaoguangcheng/sars_tutorial
Repository for the tutorial on Sequence-Aware Recommender Systems held at ACM RecSys 2018
shaoguangcheng/rtb-papers
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
shaoguangcheng/Ad-papers
Papers on Computational Advertising
shaoguangcheng/tensorflow_fasttext
Simple embedding based text classifier inspired by fastText, implemented in tensorflow
shaoguangcheng/tensorflow-triplet-loss
Implementation of triplet loss in TensorFlow