thana.lee's repositories
pyTorch-Trading
Forex Trading with Artificial Intelligent, using pytorch
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
binance-trade-bot
Automated cryptocurrency trading bot
cake_sniper
EVM frontrunning tool
ConvLSTM_pytorch
Implementation of Convolutional LSTM in PyTorch.
Convolution_LSTM_PyTorch
Multi-layer convolutional LSTM with Pytorch
CycleGAN
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
deep-learning-1
Repo for the Deep Learning Nanodegree Foundations program.
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
deeplearning-models
A collection of various deep learning architectures, models, and tips
gans-in-action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Keras-GAN
Keras implementations of Generative Adversarial Networks.
machine_learning_examples
A collection of machine learning examples and tutorials.
machiseo
มาชิสซอ
Pose-Guided-Person-Image-Generation
Tensorflow implementation of our NIPS 2017 paper "Pose Guided Person Image Generation"
python-binance
Binance Exchange API python implementation for automated trading
PyTorch-Tutorial
Build your neural network easy and fast
pytorch-tvmisc
Totally Versatile Miscellanea for Pytorch
srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
StockMarketGAN
Stock Market Prediction Using Unsupervised Features
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.
t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)