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an-introduction-to-statistical-learning-with-applications-in-r

An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science.

angular

anisbleplaybooks

anisbleroles

ansible

My ansible playbooks

ansible-1

ansible_for_beginners

ant--java-project

ant-enterprise-application

ant-javaproject

ant-standalone-application

ant-web-application

ant-webproject

awesome-docker

bicycle-sharing-system-in-us

A bicycle-sharing system, public bicycle system, or bike-share scheme, is a service in which bicycles are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" and return it at another dock belonging to the same system. Docks are special bike racks that lock the bike, and only release it by computer control. The user enters payment information, and the computer unlocks a bike. The user returns the bike by placing it in the dock, which locks it in place. Other systems are dockless. For many systems, smartphone mapping apps show nearby available bikes and open docks.

data-science-with-r

The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science.

deep-learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

devops_practice

docker-cheat-sheet

dockerfiles

expressjs

face--recognition-with-opencv

Image recognition using python Very Easy Write and to Learn

face-and-object

Extensive set of findings arguing that visual recognition of faces and objects differs critically in style of computational processing. It provides a tutorial-style review of what face-recognition researchers mean by "holistic/configural" processing and describes the multiple paradigms showing that holistic/configural processing is limited to the structural form of upright faces. It then brings together several streams of literature to present a theoretical case that holistic/configural processing and part-based processing differ in patterns of sensitivity to prior experience. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

flipcart

gradle-web-app

hello-world

honeywell

java-web-app-docker

javaproject

JavaProject

jenkins-example