Introduction Optimization is always the ultimate goal whether you are dealing with a real life problem or building a software product. I, as a …
The post Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning appeared first on Analytics Vidhya.
Source: Vidhya – Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning
R is a hugely popular language among data scientists and statisticians. One of the difficulties with open-source R is the memory constraint. All the data needs to be loaded into a data.frame. Microsoft solves this problem with the RevoScaleR package of the Microsoft R Server. Just launched this week is an EdX course on
Analyzing Big Data with Microsoft R Server.
According the syllabus:
Upon completion, you will know how to use R for big-data problems.
Full Disclosure: I work at Microsoft, and the course instructor, Seth Mottaghinejad, is one of my colleagues.
Source: 101 DS – Learn to Analyze Big Data with R – Free Course
This post is based on a post that originally appeared on Alex Rogozhnikov’s blog, ‘Brilliantly Wrong’.
We have expanded the post and will continue to do so over time – if you have a suggestion please let us know in the comments. Thanks to Alex for graciously letting us republish his work here.
Jupyter notebook, formerly known as the IPython notebook, is a flexible tool that helps you create readable analyses, as you can keep code, images, comments, formulae and plots together.
Jupyter is quite extensible, supports many programming languages and is easily hosted on your computer or on almost any server — you only need to have ssh or http access. Best of all, it’s completely free.
The Jupyter interface.
Project Jupyter was born out of the IPython project as the project evolved to become a notebook that could support multiple languages – hence its historical name as the IPython notebook. The name Jupyter is an indirect acronyum of the three core languages it was designed for: JUlia, PYThon, and R and is inspired by the planet Jupiter.
When working with Python in Jupyter, the…
Source: Dataquest – 28 Jupyter Notebook tips, tricks and shortcuts