Pinned Repositories
AMPA-Net
This is the official TensorFlow implementation of AMPA-Net
BNN
Bayesian Neural Networks
Box-Office-Prediction-using-Movie-Trailers
This repository contains the codes and research report for the Box Office Return Prediction Research Project developed during the International Masters in Data Analytics at the University of Hildesheim.
bpnn
bp 神经网络算法
Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms
In this tutorial, i will apply a bunch of various Machine Learning Algorithms on the Breast Cancer Dataset and see how each of them behaves with respect to one another.
lstm-1
Exploration of timeseries LSTM RNN prediction in pytorch, keras, tensorflow, and tensorflow.contrib.keras.
Stock-Prediction-Model_pytorch
An GRU (Gated Recurrent Unit) model that can predict stops to an extremely well accuracies. Relies on Memory retention ability of LSTM/GRU models.
TimeSeries-Regression
Comparing XGBoost, CatBoost and LightGBM on TimeSeries Regression (RMSE, R2, AIC) on two different TimeSeries datasets.
transformer
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.
transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
susululu's Repositories
susululu/Box-Office-Prediction-using-Movie-Trailers
This repository contains the codes and research report for the Box Office Return Prediction Research Project developed during the International Masters in Data Analytics at the University of Hildesheim.
susululu/Stock-Prediction-Model_pytorch
An GRU (Gated Recurrent Unit) model that can predict stops to an extremely well accuracies. Relies on Memory retention ability of LSTM/GRU models.
susululu/TimeSeries-Regression
Comparing XGBoost, CatBoost and LightGBM on TimeSeries Regression (RMSE, R2, AIC) on two different TimeSeries datasets.
susululu/transformer
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.
susululu/transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
susululu/AMPA-Net
This is the official TensorFlow implementation of AMPA-Net
susululu/ETDataset
The Electricity Transformer dataset is collected to support the further investigation on the long sequence forecasting problem.
susululu/F-FADE
susululu/FinRL-Library
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, NeurIPS 2020 DRL workshop.
susululu/FISVDD
Fast Incremental Support Vector Data Description implemented in Python
susululu/GAN-Augmentation-for-Remaining-Useful-Life-Prediction
susululu/lstm
Minimal, clean example of lstm neural network training in python, for learning purposes.
susululu/LSTM-probability-of-failure
an implementation of LSTM using Keras on predicting remaining useful life (or time to failure) of aircraft engines
susululu/LSTM-TSP
Time Series Prediction using PyTorch on the covid-19 day vs cases dataset
susululu/ml
包含机器学习基础算法与应用
susululu/MLiP_M5
susululu/PredictiveMaintenance
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
susululu/PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
susululu/PyTorchDocs
PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！
susululu/ReconNet-PyTorch
A non-iterative algorithm to reconstruct images from compressively sensed measurements.
susululu/Remaining-Useful-Life-Prediction
Remaining Useful Life prediction of machinery using a novel data wrangling method and CNN-LSTM network for prediction
susululu/RobustSTL
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)
susululu/RUL-Esimator-Remaining-Useful-Life-
My Own Project for ML
susululu/RUL-Net
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
susululu/spring-framework
Spring Framework
susululu/Stock-Market-Prediction-using-a-Hybrid-Model
Stock market prediction is important topic in economics and finance which has garnered the interest of researchers. This paper attempts to explore the usage of hybrid model to predict stock market movements. The data under con- sideration was sourced from Quandl, a repository that provides data related to the stock market for a wide variety of companies, and in order to forecast the prices of said stocks in the future, ensemble machine learning methods together with ARIMA for feature prediction have been employed. Hybrid approach for predic- tion stock price is proposed. Comparisons among ensemble learning algorithms are discussed, and interesting results has been obtained and future possibilities are touched upon in this paper. Intensive testing was done by gathering various stock market data from various sectors to explore robustness of the proposed model.
susululu/Sunspot-forecast
The number of sunseeds is predicted by combining phase space reconstruction with deep neural networks.
susululu/svm-pytorch
Linear SVM with PyTorch
susululu/TimeSeries
Implementation of deep learning models for time series in PyTorch.
susululu/torchtext-summary
torchtext使用总结，从零开始逐步实现了torchtext文本预处理过程，包括截断补长，词表构建，使用预训练词向量，构建可用于PyTorch的可迭代数据等步骤。并结合Pytorch实现LSTM.