anarayn Goto Github PK

bigbasket-scraper

Collects product data from bigbasket.com

charisma

Free, responsive, multiple skin admin template

common-utils

A collection of utilities which I've created over time, most of them are wrappers over standard libraries available customised for my convenience

computational-investing-pt-1

The HW code

cs-20si-solutions

This repository pools my solutions regarding the Tensorflow for Deep Learning Research CS 20SI Stanford course.

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

devopsbuddy

Startup-ready web skeleton

ebooks

excalidraw

Virtual whiteboard for sketching hand-drawn like diagrams

fakeapi

full-teaching

[UNMAINTAINED] FullTeaching: Teaching application with OpenVidu

gekko

A bitcoin trading bot written in node - https://gekko.wizb.it/

instant-spring-boot-kotlin-graphql-mysql-app

A production ready spring boot kotlin app with graphql support using mysql as the datasource. Supports integration testing with testcontainers, database migrations using flyway

lapdftextproject

High-level build project for all LAPDF-Text submodules

mailtrap-client

Mailtrap Client library to fetch html content of matching subject text

papers-we-love

Papers from the computer science community to read and discuss.

project-shell-scripting

A project to familiarize students with shell scripting

server

This is my current server setup where I deploy self-hosted applications.

spring-boot-samples

Spring Boot samples by Netgloo

sqlprocessor

Java library to process sql Query and provide result in different format

sqlsheet

Automatically exported from code.google.com/p/sqlsheet

stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

surveyjs_react_quickstart

React QuickStart Boilerplate - SurveyJS: Survey Library and Survey Creator

tinking

🧶 Extract data from any website without code, just clicks.

userrecon

wordpress-starter-theme

The best WordPress starter theme with a modern front-end development workflow. Based on HTML5 Boilerplate, gulp, Bower, and Bootstrap.

zerodha

Automation of zerodha for HFT