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anomaly-transformer icon anomaly-transformer

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

another-react-todo icon another-react-todo

프론트엔드 개발 공부를 할 때 흔히 만들게 되는 투두리스트

echarts icon echarts

Apache ECharts is a powerful, interactive charting and data visualization library for browser

flask-boilerplate icon flask-boilerplate

Boilerplate template for a Python Flask application with Flask-SQLAlchemy, Flask-WTF, Fabric, Coverage, and Bootstrap

flow-forecast icon flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

generative_ai_with_langchain icon generative_ai_with_langchain

Build large language model (LLM) apps with Python, ChatGPT and other models. This is the companion repository for the book on generative AI with LangChain.

getting-things-done-with-pytorch icon getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

gpt-code-ui icon gpt-code-ui

An open source implementation of OpenAI's ChatGPT Code interpreter

highcharts icon highcharts

Highcharts JS, the JavaScript charting framework

langchain-kr icon langchain-kr

LangChain 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 LangChain을 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

langchain-pgvector icon langchain-pgvector

Knowledge base Q&A program using LangChain for retrieval-augmented prompting and PGVector as vector store.

lbj-case-study-2 icon lbj-case-study-2

Designed and developed a web application which would read data from a .csv file and perform various operations such as, adding a new student, searching for a student details by the student ID and display all the students. Technology stack : ➢ Frontend - HTML5, CSS, JavaScript ➢ Backend - Python3, flask, jinja. ➢ Database - CSV file. ➢ Libraries - pandas, csv. There are few files which would work combinely to perform the operations in this case study. Below is the list of those files: A) Front end - ➢ index.html ➢ add-student.html ➢ search-student.html ➢ display-student.html B) Backend ➢ main.py c) Database ➢ students.csv

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