hungita

100-Days-Of-ML-Code

100 Days of ML Coding

12306

12306智能刷票，订票

2019iFLYTEK_MachineryLife_Baseline

ace2005-preprocessing

ACE 2005 corpus preprocessing for Event Extraction task

ai-edu

AI education materials for Chinese students, teachers and IT professionals.

Al_challenger_2018_sentiment_analysis

AI Challenger 2018 细粒度用户评论情感分析，排名17th，基于Aspect Level 思路的解决方案

Algorithm_Interview_Notes-Chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

distributeKerasExample

distribute Keras

LeetCodeAnimation

Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）

NLP_Datasets

中文NLP数据集

hungita/distributeKerasExample

distribute Keras

hungita/ace2005-preprocessing

ACE 2005 corpus preprocessing for Event Extraction task

hungita/attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

hungita/bert

TensorFlow code and pre-trained models for BERT

hungita/BERT-CH-NER

基于BERT的中文命名实体识别

hungita/bert4keras

keras implement of transformers for humans

hungita/BullshitGenerator

Needs to generate some texts to test if my GUI rendering codes good or not. so I made this.

hungita/CLUE

Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard

hungita/corpus

自然语言处理，知识图谱相关语料。按照Task细分，欢迎PR。

hungita/docs-l10n

Community translations of TensorFlow documentation

hungita/drowsinessDetector

OpenCV Drowsiness Detector using Python and Dlib

hungita/fanqiang

翻墙-科学上网

hungita/Federated-Learning-for-Human-Mobility-Models

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility

hungita/fucking-algorithm

手把手撕LeetCode题目，扒各种算法套路的裤子。English version supported! Crack LeetCode, not only how, but also why.

hungita/kdxf2019top2

2019科大讯飞工程机械赛题-亚军

hungita/Machine-Learning-on-CSIC-2010

hungita/ML-NLP

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

hungita/ml-visuals

Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

hungita/NEWS-TEXT-CLASSIFICATION

零基础入门NLP - 新闻文本分类 正式赛第一名方案

hungita/NLP-Interview-Notes

该仓库主要记录 NLP 算法工程师相关的面试题

hungita/rank4_NLP_textclassification

阿里云天池零基础入门NLP比赛_rank4选手比赛总结: https://tianchi.aliyun.com/competition/entrance/531810/introduction

hungita/rasa_chatbot

基于rasa_nlu，rasa_core，rasa_core_sdk构建的聊天机器人

hungita/Rath

自动化数据探索分析和智能可视化设计应用. Automatic insights discovery and visualization for data analysis.

hungita/simpletransformers

Transformers made simple with training, evaluation, and prediction possible with one line each. Currently supports Sequence Classification (binary, multiclass, multilabel), Token Classification (NER), and Question Answering. Built on top of the Hugging Face Transformer library.

hungita/spark_tutorial

500+ spark short code examples in jupyter notebook!

hungita/tensorflow-windows-wheel

Tensorflow prebuilt binary for Windows

hungita/tensorflow_2_tutorials

Tensorflow 2.0 tutorials

hungita/TextMatch

QA match/文本匹配/文本分类/文本embedding/文本聚类/文本检索（bow/ifidf/ngramtf-df/bert/albert/bm25/…/nn/gbdt/xgb/kmeans/dscan/faiss/….）

hungita/tianchi_nlp

天池零基础入门NLP - 新闻文本分类

hungita/transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.