ZQFORWARD's Stars
qiyuangong/leetcode
Python & JAVA Solutions for Leetcode
jiqizhixin/ML-Tutorial-Experiment
Coding the Machine Learning Tutorial for Learning to Learn
philipperemy/keras-tcn
Keras Temporal Convolutional Network.
facebook/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pykalman/pykalman
Kalman Filter, Smoother, and EM Algorithm for Python
numenta/htmpapers
Numenta published papers code and data
facebookresearch/fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
yunjey/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
Cadene/pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
hunkim/PyTorchZeroToAll
Simple PyTorch Tutorials Zero to ALL!
htm-community/htm.core
Actively developed Hierarchical Temporal Memory (HTM) community fork (continuation) of NuPIC. Implementation for C++ and Python
apachecn/ailearning
AiLearning:数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
azl397985856/leetcode
LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解,记录自己的leetcode解题之路。)
leikunx/AI_projects
I am a full-stack engineer for AI projects, glad to share my experience. pratices make the top engineer.
rasbt/deeplearning-models
A collection of various deep learning architectures, models, and tips
rhyolight/nupic.examples
Some examples I made for NuPIC
numenta/nupic-legacy
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
deepfakes/faceswap
Deepfakes Software For All
numenta/htmresearch
Experimental algorithms. Unsupported.
RiweiChen/FaceTools
一键人脸归一化处理工具,包括人脸检测,人脸关键点检测,基于关键点的人脸对齐
romanchereshnev/HuGaDB
Database for human gait analysis consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two EMG sensors were used on the quadriceps (front thigh) to measure muscle activity.