Namhoon Kim's repositories
PublicDrive
이슈에 끄적끄적
NanoReport
Daily log 2019.08 ~ 2019.09
PatternRecognition
2019년 2학기 패턴인식
2019.Spring.AI
세종대학교 2019년도 1학기 인공지능 수업
ComputerVision-Basic
컴퓨터비전 기초편
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
meching
meching
mmdetection
Open MMLab Detection Toolbox and Benchmark
Pillow
The friendly PIL fork (Python Imaging Library)
ProgrammingTA
조교하면서 까다로웠던 질문 기록
SituationClassifier
자퇴와휴학사이, 장애물 분류기 (2019)
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.