Machine Learning Catch-Up


Dataconomy » Machine Learning Newsletter

Big Data News, Events, and Expert Opinion


14 Best Python Pandas Features

Mei Xiang (L) and Tian Tian (R)Pandas is the most widely used tool for data munging. It contains high-level data structures and manipulation tools designed to make data analysis fast and easy. In this post, I am going to discuss the most frequently used pandas features. I will be using olive oil data set for this tutorial, you can download the data set from this page (scroll down to data section). Apart from serving as a quick reference, I hope this post will help you to quickly start extracting value from Pandas. So lets get started! 1) Loading Data “The Olive Oils data set has eight explanatory variables (levels of fatty acids in the oils) and nine classes(areas of Italy)”. For more information you can check my Ipython notebook. I am importing numpy, pandas and matplotlib...
Read on »

Meet Pinnability- Pinterest’s Machine Learning Tool for Personalising Your Feed

Pinnability Pinterest Recommendation Machine LearningWith the aim to offer a more nuanced content to the user, Pinterest have unveiled a machine learning tool that provides the user with the most personalized and relevant Pins. The ML tool, titled Pinnability, runs on smart feed, “and estimates the relevance score of how likely a Pinner will interact with a Pin. With accurate predictions, we prioritize those Pins with high relevance scores and show them at the top of home feed,” explains Pinterest’s software engineer Yunsong Guo. Earlier all home feed content from each source (e.g., following and Picked For You) was put together chronologically; a newer Pin from the same source would show up before an older Pin, irrespective of the degree of interest either of the pins hold to the user. It stunted the discovery...
Read on »

PayPal Gears Up with Deep Learning to Fight Cybercrime

Paypal Machine Deep Learning Hackers CybercrimeTechnological advancement is, for the most part, a wonderful thing. But as technology becomes more sophisticated, so does crime. Thankfully however, so do the methods to counter such menaces. Hui Wang is the senior director of global risk sciences at PayPal. For the last 11 years, she has seen the evolution in methods of online fraudsters. “The fraudsters we’re interacting with are… very unique and very innovative. …Our fraud problem is a lot more complex than anyone can think of,” she told GigaOM (in what is, heartbreakingly, one of the last articles they may ever publish. We’ll miss you- Ed.) Wang and her team at Paypal might have figured out way to utilize deep learning to better fight attackers who prey on online payment platforms, she tells GigaOM. Deep learning, a broader...
Read on »

An Introduction to Recommendation Engines

Introduction What is a Recommendation Engine System Hybrid NetflixI’ve previously written a lot on data mining in the abstract; now, I want to start taking you through some practical applications. Welcome to the fascinating world of the recommendation engine- this post will walk through the concepts, and later posts will teach you how to implement your own. What we will learn: I’ll begin our tour by answering four basic questions: What is a recommendation engine? What is the difference between real life recommendation engine and online recommendation engines? Why should we use recommendation engines? What are the different types of recommendation engines? What is a Recommendation Engine ? Wiki Definition: Recommendation Engines are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that user would give to an item. dataaspirant Definition:  Recommendation Engine is a...
Read on »

Google & Stanford Say Big Data & Deep Learning Are the Future of Drug Discovery

Google Stanford Machine Deep Learning Neural NetworksPande Lab at Stanford University in collaboration with Google released a paper earlier this week that focuses on how neural networks and deep learning technology could be crucial in improving the accuracy of determining which chemical compounds would be effective drug treatments for a variety of diseases. A Google Research blog post explains how in the recent past, computational methods using deep learning with neural networks have attempted to ‘replace or augment the high-throughput screening process.’ So far, virtual drug screening has used existing data on studied diseases; but the volume of experimental drug screening data across many diseases continues to grow. The paper titled “Massively Multitask Networks for Drug Discovery,” among other things, quantifies how the amount and diversity of screening data from a variety of diseases with very...
Read on »

A Neural Network That Can Outsmart Wine Snobs at Their Own Game

A Neural Network That Can Outsmart Wine Snobs at Their Own GameWhat does a machine learning scientist who also happens to be a wine enthusiast come up with after a chance encounter with a legendary Bordeaux wine? An application that might be able to predict the quality of wine. Alex Tellez was a Machine Learning Scientist at Robert Half and a Senior Data Scientist at Switchfly before that. Now he is a Cyclist, Applications & Community Hacker at H2O, and he has been up to some serious ML application. “I thought, can I apply some machine learning to Bordeaux wine?” notes Tellez, according to a report in Datanami. Tellez found that the Bordeaux quality in the years 2009 and 2010 surpassed that of any in the previous 5 decades. He worked on the possibility that there may be a link between...
Read on »

Copyright © *|CURRENT_YEAR|* *|LIST:COMPANY|*, All rights reserved.

Our mailing address is:

unsubscribe from this list    update subscription preferences