Pinned Repositories
Attention
An implementation of self-attention layer via Keras
Books-Thesis_I_read
NLP/DL/ML/DS fields thesis and books
Item-name-recognition
Enabling recognize item name inside an email(or a web page) with N-gram and Neural Network
K-prototypes
K-prototypes is an un-supervised algorithm for doing clustering on data with both numerical data type and categorical data type.
KnowledgeGraph
Extract and build relation among Chinese words
MNIST-K-means-clustering
Using K-means clustering to categorize handwritten numbers
Research
SimilarCharactor
对常用的6700个汉字进行音、形比较,输出音近字、形近字的列表。
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
stopwords-en
English stopwords collection
ZhuoyangZhan's Repositories
ZhuoyangZhan/K-prototypes
K-prototypes is an un-supervised algorithm for doing clustering on data with both numerical data type and categorical data type.
ZhuoyangZhan/Attention
An implementation of self-attention layer via Keras
ZhuoyangZhan/KnowledgeGraph
Extract and build relation among Chinese words
ZhuoyangZhan/Books-Thesis_I_read
NLP/DL/ML/DS fields thesis and books
ZhuoyangZhan/Item-name-recognition
Enabling recognize item name inside an email(or a web page) with N-gram and Neural Network
ZhuoyangZhan/MNIST-K-means-clustering
Using K-means clustering to categorize handwritten numbers
ZhuoyangZhan/Research
ZhuoyangZhan/SimilarCharactor
对常用的6700个汉字进行音、形比较,输出音近字、形近字的列表。
ZhuoyangZhan/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
ZhuoyangZhan/stopwords-en
English stopwords collection
ZhuoyangZhan/Text2Image
A tool for converting text/character into image(png)