sudae827 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.

accel-brain-code

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

air-quality

Air Quality Forecasting for Beijing

air-quality-forecasting-madrid

air_pollution_forcast_beijing

基于Keras的LSTM多变量时间序列预测

awesome-attention-mechanism-in-cv

:punch: 计算机视觉中用到的注意力模块和其他即插即用模块PyTorch Implementation Collection of Attention Module and Plug&Play Module

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

deep-learning--forecasting-air-pollution-in-beijing-china-machine-learning-

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

deep-learning-with-pytorch-tutorials

深度学习与PyTorch入门实战视频教程 配套源代码和PPT

eat_pytorch_in_20_days

Pytorch🍊🍉 is delicious, just eat it! 😋😋

leetcode-python

LeetCode solutions in Python2. LeetCode题解 in Python2。

mhms_deeplearning

ml_pm2.5

Machine Learning Models to forecast PM 2.5

paddle

PArallel Distributed Deep LEarning

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！

seq2seq

Sequence to Sequence Learning with Keras

solve-air-air-pollution-forecasting-using-deep-attentive-sequence-learning

First step towards solving a real-life problem - air pollution forecasting in Delhi, using deep learning

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.

svm_nusvr

Nu Support Vector Regression

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

timeseries_seq2seq

This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.

wrapper-feature-selection-toolbox-python

This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.