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157711's Projects

ant-design icon ant-design

An enterprise-class UI design language and React UI library

awesome-python icon awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

buck icon buck

A fast build system that encourages the creation of small, reusable modules over a variety of platforms and languages.

chart.js icon chart.js

Simple HTML5 Charts using the <canvas> tag

deeptrade icon deeptrade

A LSTM model using Risk Estimation loss function for stock trades in market

elf icon elf

ELF: a platform for game research

ganhacks icon ganhacks

starter from "How to Train a GAN?" at NIPS2016

katago icon katago

GTP engine and self-play learning in Go

leela-zero icon leela-zero

Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.

lstm-fcn icon lstm-fcn

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

miopen icon miopen

AMD's Machine Intelligence Library

odoo icon odoo

Odoo. Open Source Apps To Grow Your Business.

ollama icon ollama

Get up and running with Llama 3, Mistral, Gemma, and other large language models.

open-webui icon open-webui

User-friendly WebUI for LLMs (Formerly Ollama WebUI)

paddleocr icon paddleocr

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

phoenixgo icon phoenixgo

Go AI program which implement the AlphaGo Zero paper

probability icon probability

Probabilistic reasoning and statistical analysis in TensorFlow

rag icon rag

RAG (Retrieval-Augmented Generation) Chatbot Examples Using PyMuPDF

stockpredictionai icon 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.

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