R for Data Science

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

Source: R for Data Science

 

Building a Deep Learning (Dream) Machine

As a PhD student in Deep Learning, as well as running my own consultancy, building machine learning products for clients I’m used to working in the cloud and will keep doing so for production-oriented systems/algorithms. There are however huge drawbacks to cloud-based systems for more research oriented tasks where you mainly want to try out various algorithms and architectures, to iterate and move fast. To make this possible I decided to custom design and build my own system specifically tailored for Deep Learning, stacked full with GPUs. This turned out both more easy and more difficult than I imagined. In what follows I will share my “adventure” with you. I hope it will be useful for both novel and established Deep Learning practitioners.

Source: I: Building a Deep Learning (Dream) Machine

 

Advancing in the Bash Shell – ${me:-whatever}

Source: Advancing in the Bash Shell – ${me:-whatever}

 

Don’t Start Big, Start a Little Snowball

When myself and my co-host interviewed Travis Kalanick on our podcast, he had recently co-founded a little snowball called UberCab. It was so early in Uber’s existence he didn’t even mention it.

I notice Uber falls into a category of companies I call little snowballs. There are some fundamental features these companies have in common. I thoguht it might be helpful to list a few little snowballs and then talk about how you can go about starting your own.

Uber
Travis Kalanick and Garrett Camp commissioned the creation of a simple app that enabled users to hail a black car on demand. To validate they asked friends and colleagues to install the app. They hired a driver & watched what happened. After a few months of hustle the app hit 10 rides in one day.

Airbnb
Brian Chesky and Joe Gebbia bought a few airbeds and put up a static site called “Air Bed and Breakfast”. They expanded the concept so that other people could offer airbeds in their homes and make money too. Then they expanded the idea to rooms, then to entire apartments. Their journey was a hustle from hell, but they made it happen.

Google
Sergey Brin and Larry Page built a web search engine. It was one page with a search box and submit button. The backend was a simple database search returning ranked results based on the number of backlinks. It did a really good job at providing search results, and took off.

Slack
Stewart Butterfield worked with his Tiny Speck group to build a better team messaging app. Slack makes it really easy for people to signup and start chatting as a team. At it’s core Slack is a simple IM client.

Other examples
AT&T, Dollar Shave Club, Buffer, Shazam, DropBox

Source: Don’t Start Big, Start a Little Snowball

 

Channels adopted as an official Django project

The Django team is pleased to announce that the Channels project is now officially part of the Django project, under our new Official Projects program. Channels is the effort to bring WebSockets, long-poll HTTP, and other non-request-response protocol and business logic handling to Django, as part of our ongoing effort to establish what makes a useful web framework in 2016.

Official projects, like Channels, do not merge into the core django repository but instead remain as separate repositories and packages, living under the Django organization on GitHub. They have their own release schedule and backwards compatibility policies, but fall under the main Django security policy and oversight, and are guaranteed to work with the currently supported versions of Django.

While the Channels project was initially targeted to be included in Django 1.10, it didn’t make it for a variety of reasons. We decided that the best move would be to bring it under the Django umbrella, but keep it separate from the core repository, and so DEP 7 and the Official Projects track was born to enable this. DEP 7 describes what it means to maintain a Django package, so we can make sure they stay updated, have security issues patched, and work with current Django releases.

Source: Channels adopted as an official Django project | Weblog | Django

 

Oh, shit, git!

Git is hard: screwing up is easy, and figuring out how to fix your mistakes is fucking impossible. Git documentation has this chicken and egg problem where you can’t search for how to get yourself out of a mess, unless you already know the name of the thing you need to know about in order to fix your problem.

So here are some bad situations I’ve gotten myself into, and how I eventually got myself out of them in plain english.

Source: Oh, shit, git!