youjp's repositories
tensorflow_models_learning
tensorflow GoogleNet inception V1 V2 V3 V4
deep-orderbook
Deep learning modelling of orderbooks
Deep-Time-Series-Prediction
Seq2Seq, Bert, Transformer, WaveNet for time series prediction.
Transformer_Time_Series
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)
delight
DeLighT: Very Deep and Light-Weight Transformers
translob
Transformers for limit order books
n-beats
Pytorch/Keras implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
deep-stock
Deep Learning for Stock Market
ConvNets-TensorFlow2
⛵️ Implementation a variety of popular Image Classification Models using TensorFlow2. [ResNet, GoogLeNet, VGG, Inception-v3, Inception-v4, MobileNet, MobileNet-v2, ShuffleNet, ShuffleNet-v2, etc...]
op_importance
Compute set of important operations for HCTSA code
TCN
Sequence modeling benchmarks and temporal convolutional networks
SOFTX_2020_1
An intuitive library to extract features from time series. To cite this software publication: https://www.sciencedirect.com/science/article/pii/S2352711020300017
NLP-Project
including text classifier, language model, pre_trained model, multi_label classifier, text generator, dialogue. etc
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
Inceptionv4_and_Inception-ResNetv2.PyTorch
A PyTorch implementation of Inception-v4 and Inception-ResNet-v2.
hyperparameter_hunter
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
kungfu
Kungfu Trader
poboquant
quant strategy backtesting from pobo financial
activityrecognition
Resources about activity recognition-行为识别资料
starquant
a light-weighted, integrated trading/backtesting system/platform（综合量化交易回测系统/平台）
Skater
Python Library for Model Interpretation/Explanations
OrderBook
Matching Engine for Limit Order Book
git_exercise
used for git test
scikit-feature
open-source feature selection repository in python (DMML Lab@ASU)
FES
Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson
nnsubspace
Uncertainty Propagation in Deep Neural Network Using Active Subspace
OctaveConv_pytorch
Pytorch implementation of Octave convolution
featuretools
An open source python framework for automated feature engineering
practical-machine-learning-with-python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.