pjus Goto Github PK

pixel2mesh

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images. In ECCV2018.

pjus.github.io

plant-disease-detection

A plant disease detector (classifier) based on the PlantVillage dataset.

port2023-vite

predict_loan_defaults

present

Code from Jeff Heaton's YouTube videos, articles, and conference presentations.

py4fi

Python for Finance (O'Reilly)

python-machine-learning-book-2nd-edition

<머신러닝 교과서 with 파이썬, 사이킷런, 텐서플로>의 코드 저장소

python_class_practice

class practice

pytorch-computer-vision-cookbook

PyTorch Computer Vision Cookbook, Published by Packt

pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

quantresearch

Quantitative analysis, strategies and backtests

real-time-voice-cloning

Clone a voice in 5 seconds to generate arbitrary speech in real-time

reinforcement-learning-kr

[파이썬과 케라스로 배우는 강화학습] 예제

rl_stock

rltrader

파이썬과 케라스를 이용한 딥러닝/강화학습 주식투자 - 퀀트 투자, 알고리즘 트레이딩을 위한 최첨단 해법 입문 (개정판)

rltrader-1

A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

sc-fegan

SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color (ICCV2019)

sd_study

SD 아카데미 공부자료

smart-farm-

Farm2.0 is a smart project that allows to interact and improve the management of agricultural farms.

social

sql_db

SQL 공부

standalone-deeplearning

2019 KAIST 딥러닝 홀로서기 세미나용 저장소입니다.

stargan

StarGAN - Official PyTorch Implementation (CVPR 2018)

stock-fundamental-data-scraping-and-analysis

Project on building a web crawler to collect the fundamentals of the stock and review their performance in one go

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stockanalysisapp

A stock analysis app with streamlit

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.

stockproject