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interview

Everything you need to prepare for your technical interview

leetcode

LeetCode Problems' Solutions

leetcode-solutions

:octocat: (Weekly Update) Python / C++11 Solutions of All 1284 LeetCode Problems

lightfieldreconstruction

High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction

llnet

A low light image enhancement with deep learning

lowlightimageenhancement

Low Light Image Enhancement

noise2noise-pytorch

PyTorch Implementation of Noise2Noise (Lehtinen et al., 2018)

onlineshop

Online Shop Project for training ASP.NET MVC course

patchcore-inspection

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

python-tesseract-3.02-training

A script to partially automate the training of new fonts/languages with Tesseract 3.02

python_book

any body here python tutorial and book can contribute here

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch-vdsr

VDSR (CVPR2016) pytorch implementation

pytorch-zssr

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

recommendersystem

Python implementation of Recommender System

reinforcement-learning

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

reinforcement-learning-in-process-control

Master thesis spring 2019. Template to be futher used by the department of chemical engineering at NTNU,

resnetpy

Test ResNetPy

seaborntutorial

Seaborn Basic Tutorial

single-image-super-resolution-sisr

A list of resources for example-based single image super-resolution

spccontrolclassification

StatisticalProcess Control (SPC) charts also known as Shewhartcharts have been used widely in many processes. However, with so many control charts in manufacturing processes, process engineers find the difficulty to interpret/classify control charts into the specific categories. Several attempts have been made to classify control charts automatically but there is still a room for higher recognition accuracy. This paper demonstrates how effectively support vector machines as supervised learning can be used in SPC Control Chart classification. However, in reality, labeled data always is limited and expensive whereas unlabeled data is obviously cheap and available. In this study, we also present a new approach to classify control chart using Label Spreading as semi-supervised learning for unlabeled data.

spmc_videosr-pytorch

PyTorch Implementation of [Detail-revealing Deep Video Super-resolution](https://arxiv.org/abs/1704.02738)

srgan

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

srmd

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018)

system-design-interview

System design interview for IT companies

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

teducoreapp

Tedu Core App For Course TEDU-17

time-series-forecast

Time Series Forecast using GluonTS, FBProphet and Deep Learning with AutoKeras

tlpi

The Linux Programming Interface