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rl-course-by-david-silver

rl-study

personal repo. for reinforcement learning study

rl_pytorch

rul-net

Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine

self-study

on-going personal study

sguys99

sguys99.github.io

personal blog

stock-market-analysis

stockanalysisinpython

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.

streamlit_dashboard

streamlit_study

svdd-python

Python code for abnormal detection or fault detection using Support Vector Data Description (SVDD)

tensorflow-101

TensorFlow Tutorials

tensorflow-project-template

A best practice for tensorflow project template architecture.

test-pr

Repository to test pull request

timeseries-study

tods

TODS: An Automated Time-series Outlier Detection System

unsupervised-deep-learning-framework-for-anomaly-detection-in-time-series-

Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based) settings for anaomaly detection in time series data

unsupervised-learning

vision

vision_study

x-transformers

A simple but complete full-attention transformer with a set of promising experimental features from various papers

xgboost_study

study materials for xgboost