- Google’s Seven Robotics Companies (IEEE) — The seven companies are capable of creating technologies needed to build a mobile, dexterous robot. Mr. Rubin said he was pursuing additional acquisitions. Rundown of those seven companies.
- Hebel (Github) — GPU-Accelerated Deep Learning Library in Python.
- What We Learned Open Sourcing — my eye was caught by the way they offered APIs to closed source code, found and solved performance problems, then open sourced the fixed code.
ENTRIES TAGGED "Python"
It has roots in academic scientific computing, but has features that appeal to many data scientists
As I noted in a recent post on reproducing data projects, notebooks have become popular tools for maintaining, sharing, and replicating long data science workflows. Much of that is due to the popularity of IPython1. In development since 2001, IPython grew out of the scientific computing community and has slowly added features that appeal to data scientists.
Roots in academic scientific computing
As IPython creator Fernando Perez noted in his “historical retrospective”, exploratory analysis in a scientific setting requires a solid interactive environment. After years of development IPython has become a great tool for interacting with data. IPython also addresses other important pain points for scientists – reproducibility and collaboration – issues that are equally important to data scientists working in industry.
IPython is more than just Python
With an interactive widget architecture that’s 100% language-agnostic, these days IPython is used by many other programming language communities2, including Julia, Haskell, F#, Ruby, Go, and Scala. If you’re a data scientist who likes to mix-and-match languages, you can create, maintain, and share multi-language data projects in IPython:
Assertions, regression tests, and version control
Programming any non-trivial piece of software feels like rock climbing up the side of a mountain. The larger and more complex the software, the higher the peak.
You can’t make it to the top in one fell swoop, so you need to take careful steps, anchor your harnesses for safety, and set up camp to rest. Each time you start coding on your project, your sole goal is to make some progress up that mountain. You might struggle a bit to get set up at first, but once you get going, progress will be fast as you get the basic cases working. That’s the fun part; you’re in flow and slinging out dozens of lines of code at a time, climbing up that mountain step by steady step. You feel energized.
However, as you keep climbing, it will get harder and harder to write each subsequent line. When you run your program on larger data sets or with real user inputs, errors arise from rare edge cases that you didn’t plan for, and soon enough, that conceptually elegant design in your head gives way to a tangled mess of patches and bug fixes. Your software starts getting brittle and collapsing under its own weight.
Flexible Data, Google's Bottery, GPU Assist Deep Learning, and Open Sourcing
Offline Design, Full Text, Parsing Library, and Node Streams
- Network Connectivity Optional (Luke Wroblewski) — we need progressive enhancement: assume people are offline, then enhance if they are actually online.
- Whoosh — fast, featureful full-text indexing and searching library implemented in pure Python
- Flanker (GitHub) — open source address and MIME parsing library in Python. (via Mailgun Blog)
- Stream Adventure (Github) — interactive exercises to help you understand node streams.
Unlocking Scientific Data with Python
Most people working on complex software systems have had That Moment, when you throw up your hands and say “If only we could start from scratch!” Generally, it’s not possible. But every now and then, the chance comes along to build a really exciting project from the ground up.
In 2011, I had the chance to participate in just such a project: the acquisition, archiving and database systems which power a brand-new hypervelocity dust accelerator at the University of Colorado.
Scan Win, Watson Platform, Metal Printer, and Microcontroller Python
- Google Wins Book Scanning Case (Giga Om) — will probably be appealed, though many authors will fear it’s good money after bad tilting at the fair use windmill.
- IBM Watson To Be A Platform (IBM) — press release indicates you’ll soon be able to develop your own apps that use Watson’s machine learning and text processing.
- MiniMetalMaker (IndieGogo) — 3D printer that can print detailed objects from specially blended metal clay and fire.
- MicroPython (KickStarter) — Python for Microcontrollers.
Coding for Unreliability, AirBnB JS Style, Category Theory, and Text Processing
- Quantitative Reliability of Programs That Execute on Unreliable Hardware (MIT) — As MIT’s press release put it: Rely simply steps through the intermediate representation, folding the probability that each instruction will yield the right answer into an estimation of the overall variability of the program’s output. (via Pete Warden)
- Category Theory for Scientists (MIT Courseware) — Scooby snacks for rationalists.
- Textblob — Python open source text processing library with sentiment analysis, PoS tagging, term extraction, and more.
Recharging the Python standard library
It’s unfortunate that the official About Python page still describes Python’s standard library as having “batteries included.” Sure, some of those old standbys will keep your project going and going, but many of them are leaking acid all over the place. Guido Van Rossum, head developer of Python, has said “the stdlib offerings … are not very convenient and may not support popular idioms very well.” Five years ago, I always assumed the Python library contained the “best of breed” for all packages. These days, I tend to think the opposite.
To counteract this minor flaw, I keep a small “personal standard library.” I keep a pip requirements file listing all the packages I use in every project. A simple script automatically installs that file whenever I create a virtualenv for a new project. With the pip download cache enabled, this is a near-painless process.