This post introduces a new feature on Polyaxon for checkpointing, resuming, and restarting experiments.

The AYLIEN News API gathers, analyzes, and indexes stories from tens of thousands of publishers as they are published, giving us a vast, live dataset that is updated in near-real time.

(c) 2018 by Thomas Wiecki

Did you know that every month, a random Locke Data Twitter follower wins a nifty data science book? If you don’t, and you don’t follow Locke Data on Twitter yet, do it! This month’s book was “Tidy text mining” by Julia Silge and David Robinson, a fantastic introduction to Natural Language Processing in R. If you haven’t been lucky enough to score a paperback version, you can read it online for free! You can check out tweets about previous give-aways via this Twitter moment and see that on top of giving away great books we also try to make the winner announcement fun. In this post I shall explain how we announced this month’s winner with an animated gif of followers’ screennames using the particles package.

Today, just about all monitors and screens are digital (typically using an LCD or Plasma technology), but a decade or two ago, computer displays were based on the analog technology inherited from TV sets.

Introduction

Last week, we introduced 360° Stance Detection, a model that classifies the attitude of a journalist toward an entity in a story as either ‘for,’ ‘against,’ or ‘neutral’. Specifically, we talked about how it could be used to combat filter bubbles by recommending news content to media consumers containing different points of view to what they have already read. This week, we’re going to try the model out on a data science use case and analyze the stances taken by CNN and Fox toward President Donald Trump.

This weekend we ran PyDataLondon 2018, the fifth iteration of our conference (connected with our monthly PyDataLondon meetup). This year we grew to 500 attendees! Read about the past PyDataLondon 2017 here.

63% of people use Gensim at work, and 48% work for a commercial company. The most popular industries are Media & Advertising, IT and Customer Support. Plus there’s a number of people applying Gensim in Medical, Legal, HR and Fintech.

You’ve decided that you want to bike from your house by UC Berkeley to the Golden Gate Bridge. It’s a nice 20 mile ride, but there’s a problem: you’ve never ridden a bike before! To make matters worse, you are new to the Bay Area, and all you have is a good ol’ fashion map to guide you. How do you get started?