Pinned Repositories
autonomous-learning-library
A PyTorch library for building deep reinforcement learning agents.
coursera-deep-learning
Solutions to all quiz and all the programming assignments!!!
Deep-Learning-Specialization-Coursera
A deep learning specialization series of 5 courses offered by Andrew Ng at Coursera
Deep-Learning-Specialization-Coursera-1
Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.
Deep-Reinforcement-Learning-Application-in-Finance
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020.
deepdow
Portfolio optimization with deep learning.
deeptrading
Deep Neural Network Trading collection of Tensorflow Jupyter notebooks
Financial-News-for-Stock-Prediction-using-DP-LSTM-NIPS-2019
Differential Privacy-inspired LSTM for Stock Prediction Using Financial News. NeurIPS Robust AI in Financial Services 2019.
Grokking-Deep-Learning
this repository accompanies the book "Grokking Deep Learning"
kyawkyawkhaing's Repositories
kyawkyawkhaing/Financial-News-for-Stock-Prediction-using-DP-LSTM-NIPS-2019
Differential Privacy-inspired LSTM for Stock Prediction Using Financial News. NeurIPS Robust AI in Financial Services 2019.
kyawkyawkhaing/Grokking-Deep-Learning
this repository accompanies the book "Grokking Deep Learning"
kyawkyawkhaing/autonomous-learning-library
A PyTorch library for building deep reinforcement learning agents.
kyawkyawkhaing/coursera-deep-learning
Solutions to all quiz and all the programming assignments!!!
kyawkyawkhaing/Deep-Learning-Specialization-Coursera
A deep learning specialization series of 5 courses offered by Andrew Ng at Coursera
kyawkyawkhaing/Deep-Learning-Specialization-Coursera-1
Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.
kyawkyawkhaing/Deep-Reinforcement-Learning-Application-in-Finance
kyawkyawkhaing/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020.
kyawkyawkhaing/deepdow
Portfolio optimization with deep learning.
kyawkyawkhaing/deeptrading
Deep Neural Network Trading collection of Tensorflow Jupyter notebooks
kyawkyawkhaing/dlaicourse
Notebooks for learning deep learning
kyawkyawkhaing/DO101-apps
kyawkyawkhaing/finance-and-risk-management
applications for risk management through computational portfolio construction methods
kyawkyawkhaing/hello-nodejs
kyawkyawkhaing/intro-to-dl
Resources for "Introduction to Deep Learning" course.
kyawkyawkhaing/PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
kyawkyawkhaing/portfolio_optimization
kyawkyawkhaing/practical-reinforcement-learning
kyawkyawkhaing/py-quantmod
Powerful financial charting library based on R's Quantmod | http://py-quantmod.readthedocs.io/en/latest/
kyawkyawkhaing/reinforcement-learning-an-introduction
Python implementation of Reinforcement Learning: An Introduction
kyawkyawkhaing/Reinforcement_Learning_for_Stock_Prediction
This is the code for "Reinforcement Learning for Stock Prediction" By Siraj Raval on Youtube
kyawkyawkhaing/stock-prediction
Stock price prediction with recurrent neural network. The data is from the Chinese stock.
kyawkyawkhaing/stock-price-anfis
An ANFIS Model for Stock Price Prediction
kyawkyawkhaing/stock_market_reinforcement_learning
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
kyawkyawkhaing/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.