jinhuizhang,Jinhui ZHANG,github

3d_multiview_reg

Learning multiview 3D point cloud registration

coding-interview-gym

leetcode.com , algoexpert.io solutions in python and swift

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases.

glove

GloVe model for distributed word representation

graph-wavenet

graph wavenet

gru_ode_bayes

Pytorch implementation of GRU-ODE-Bayes

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

individual-household-electric-power-consumption-data-set-

Individual household electric-power consumption Data Set (LSTM) [tutorial]

keras_auto

How to perform Keras hyperparameter optimization x3 faster on TPU for free

leetcode-solutions

:octocat: (Weekly Update) Python / Modern C++ Solutions of All 1825 LeetCode Problems

lstm-time-series-prediction-pytorch

Long Short Term Memory unit (LSTM) was typically created to overcome the limitations of a Recurrent neural network (RNN). The Typical long data sets of Time series can actually be a time-consuming process which could typically slow down the training time of RNN architecture. We could restrict the data volume but this a loss of information. And in any time-series data sets, there is a need to know the previous trends and the seasonality of data of the overall data set to make the right predictions.