dee-z Goto Github PK

a-stock-prediction-system-with-deep-learning

Try to predict stock price with LSTM、GAN and DRL, exploring the features of news and technical indicators，which help improving perfomance of predictions.

adversarial-sample-generation

Deep Generative Model Class Project, use cVAE + RNN(GRU/LSTM) + GAN to produce adversarial sentences

awesome-interpretable-machine-learning

breakoutdetection

Breakout Detection via Robust E-Statistics

electricitytheftdetection

Electricity-Theft Detection in Smart Grids

etdataset

The Electricity Transformer dataset is collected to support the further investigation on the long sequence forecasting problem.

gan-convlstm

GAN based ConvLSTM in precipitation prediction (Trial Version)

gans-with-recurrent-connections-lstm-gru-

Part of my Master Thesis in Autonomous Driving.

generative-adversarial-networks-and-gated-recurrent-units

This repository consists of implementation of GAN and GRU

give-me-some-credit

Python | XGBoost | Shap

interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

keras_deeplearningwithai_model

AI model example

lhy_dl_hw

lifespan_age_transformation_synthesis

Lifespan Age Transformation Synthesis code

lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

lstm-gan-

The LSTM GAN model can be used for generation of synthetic multi-dimension time series data.

machine-learning-yearning-cn

Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著

math-of-neural-networks

Create a few popular Neural Networks from scratch using just Numpy

ml_mindmap

mnist-dataset

Tensorflow(MLP, CNN, LSTM, GRU, GAN), Keras(MLP, CNN, LSTM, GRU, GAN)

motion-prediction---pytorch

This is implemented for moving object prediction; using CNN, GAN and LSTM

multinomial-classification-with-shapley-value

Example of Supervised Machine Learning (Multinomial Classification) using XGBoost and Shapley Value in Python

multires

Deep learning models for doing predictions based on multi resolution, irregular and partially missing time-series data sets

paperproject

All about the code mentioned in the paper

shap

A game theoretic approach to explain the output of any machine learning model.

shap_demo

Demo of shapely values for interpreting decision tree model

shap_xgboost

Feature importance using XGBoost

shapeletpy

Time Series Classification Using Shapelets

stock-market-prediction-using-gan

#LSTM #1D CNN #GAN #Stock Prediction #Indicators #AMD #FinanceDataReader #Crawling

stock-market-prediction-using-gan-1

#LSTM #1D CNN #GAN #Stock Prediction #Indicators #AMD #PlaidML #Crawling