chiragasourabh Goto Github PK

armv7l-wheels

blackcater

Using Node.js to generate my Github profile readme automatically.

c_cpp

basic C and CPP

classify-the-lunar-rock

Transfer Learning

covid-19-primary-analysis

cpr

disease-prediction

Using Decision Tree

emoji-cheat-sheet

A markdown version emoji cheat sheet

enterprise_gateway

A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across distributed clusters such as Apache Spark, Kubernetes and others.

final-year-project

garden-nerd-flower-recognition

gautamkrishnar

Yet another GitHub profile readme :smiley:

github-profile-trophy

🏆 Add dynamically generated GitHub Stat Trophies on your readme

githubgraduation-2022

Join the GitHub Graduation Yearbook and "walk the stage" on June 11.

hdfc

jupyter-resource-usage

Jupyter Notebook Extension for monitoring your own Resource Usage

jupyter_server

The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications.

kiteconnectjs

The official NodeJs client library for the Kite Connect trading APIs

lgbm-train

ml-lab

Lab Programs

ml-search-engine

web application using flask framework

mlflow

Open source platform for the machine learning lifecycle

mlw-lib

MLW-Lib

nats-site

Website content for https://nats.io. For technical issues with NATS products, please log an issue in the proper repository.

node-red-contrib-alpaca

Node-RED integration for Alpaca trading API

node-red-contrib-influxdb

Node-RED nodes to save and query data from an influxdb time series database

node-red-contrib-kiteconnect

Node-RED integration for Kite Connect API

node-red-contrib-nixtla

Node-RED integration for nixtla

notebook

Jupyter Interactive Notebook

nyoka

Nyoka is a Python library for comprehensive support of the latest PMML standard plus extensions for data preprocessing, script execution and highly compacted representation of deep neural networks. Using Nyoka, Data Scientists can export a large number of Machine Learning and Deep Learning models from popular Python frameworks into PMML by either using any of the numerous included ready-to-use exporters or by creating their own exporter for specialized/individual model types by simply calling a sequence of constructors.