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anlp

anlp-1

Applied Natural Language Processing

anlpir-2018

GitHub repository for ANLPIR 2018 course.

camstyle

Camera Style Adaptation for Person Re-identification CVPR 2018

clothes-classification

CNN for Clothes Category and Style Classification

deep-fashion-retrieval

Simple image retrival on deep-fashion dataset with pytorch - A course project

deepfashion

Apparel detection using deep learning

iaml

Introductory Applied Machine Learning (INFR09029)

iaml-1

This repository contains all the homework and assignments that given in the course called Introductory to Applied Machine Learning(IAML).

iaml-2

sklearn/seaborn Jupyter Notebooks from Interactive Applied Machine Learning Course Projects

iaml-3

Machine Learning Projects (INFR 10069)

iaml2017

Introductory Applied Machine Learning (INFR09029)

imageclassification-pytorch

keras-pytorch-avp-transfer-learning

We pit Keras and PyTorch against each other, showing their strengths and weaknesses in action. We present a real problem, a matter of life-and-death: distinguishing Aliens from Predators!

language-models

Build unigram and bigram language models, implement Laplace smoothing and use the models to compute the perplexity of test corpora.

laser_dependencies

mlpr_a1

UoE - MLPR Assignment 1

mlpr_malaria_detection

Example of image classification for Machine Learning and Pattern Recognition lecture.

mlpractical

Machine Learning Practical course repository

mlprague18-nlp

NLP Tutorial Workshop at MLPrague 2018

mlprague2018_tutorial

Code and Datasets for the "Deep Learning for Music Classification using Keras" Workshop

msc-project

natural-language-processing

Language Modelling, CMI vs Perplexity

offer-coding-interviews-python

剑指Offer Python题解

part_reid

Code for ICCV2017 paper: Deeply-Learned Part-Aligned Representations for Person Re-Identification

projarl_tum2016

project to teach robot play soccer ball via reinforcement learning

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

random-erasing

Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST