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headline_generation_2018

2018年新的标题生成

healingl.github.io

我的个人博客，记录一些项目或者学习中遇到的问题

kaggle

kaggle_talkingdata

kaggle中CTR比赛第十一名(https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection/shiyim

keras-text-summarization

Text summarization using seq2seq in Keras

leetcode-python-algorithm

使用python刷leetcode

lstm-gru-cnn-mlp

Trying to use LSTM/GRU/CNN/MLP in keras to do quantized speech reconstruction using classification methods.

machinelearning

机器学习相关的知识点思考总结以及资料收集，持续更新。

machinelearning_python

机器学习算法python实现

news-headline-generation

Our goal is to implement text summarization by generating headline for a news body using recurrent neural networks. We attempt to reproduce the results in the paper: https://arxiv.org/pdf/1512.01712.pdf

nlp_battle

keras 模型、

ogeek_stopwords

Ogeek比赛使用的停用词

opencv

Programs showing the various functionalities of OpenCV python

opengl

picking-cube-by-colorbuffer

python-basic-algorithm-and-data-structure

使用python实现的基础算法及数据结构

pytorch-doc-zh

PyTorch 中文文档

pytorchtext

1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)

recurrent-convolutional-neural-network-text-classifier

TextRCNN keras

seq2seq

Sequence to Sequence Learning with Keras

seq2seq_attention

Sequence to sequence with attention model implemented in Keras

simple_nlp_chinese

中文的word2vec以及doc2vec模型，使用维基百度的数据训练。供大家参考

sohu-2018-4th-place-solution

2018搜狐内容识别算法大赛-解决方案（4th）

sparkling-titanic

Training models with Apache Spark, PySpark for Titanic Kaggle competition

student-grants

DataCastle 大学生助学金精准资助预测（样本不均衡条件下的多分类任务）第7/352名解决方案

system-design-primer

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

talkingdata

kaggle中CTR比赛第四名(https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection/)

talkingdata-adtracking-fraud-detection

kaggle中CTR比赛第一名(https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection/)

tencent_ads_algo_2018

腾讯2018广告算法第3名

text-frites

Playing with NLP techniques with the ~500,000 Amazon fine food reviews.