There are many tutorials with directions for how to use your Nvidia graphics card for GPU-accelerated Theano and Keras for Linux, but there is only limited information out there for you if you want to set everything up with Windows and the current CUDA toolkit. This is a shame however because there are a large number of computers out there with very nice video cards that are only running windows, and it is not always practical to use a Virtual Machine, or Dual-Boot. So for today’s post we will go over how to get everything running in Windows 10 by saving you all the trial and error I went through. (All of these steps should also work in earlier versions of Windows).
A previous post showed beginners how to try out deep learning libraries by
- using an Amazon Machine Image (AMI) pre-installed with deep learning libraries
- setting up a Jupyter notebook server to play with said libraries
If you have VirtualBox and Vagrant, you can follow a similar procedure on your own computer. The advantage is that you can develop locally, then deploy on an expensive AWS EC2 gpu instance when your scripts are ready.
Want a quick and easy way to play around with deep learning libraries? Puny GPU got you down? Thanks to Amazon Web Services (AWS) — specifically, AWS Elastic Compute Cloud (EC2) — no data scientist need be left behind.
Jupyter/IPython notebooks are indispensable tools for learning and tinkering. This post shows how to set up a public Jupyter notebook server in EC2 and then access it remotely through your web browser, just as you would if you were using a notebook launched from your own laptop.
Reinstalling software and configuring settings on a new computer is a pain. After my latest hard drive failure set the stage for yet another round of download-extract-install and configuration file twiddling, it was time to overhaul my approach. "Enough is enough!"
This post walks through
- how to back up and automate the installation and configuration process
- how to set up a minimal framework for data science
We’ll use a dotfiles repository on Github to illustrate both points in parallel.
We have made use of Python’s Pandas package in a variety of posts on the site. These have showcased some of Pandas’ abilities including the following:
- DataFrames for data manipulation with built in indexing
- Handling of missing data
- Data alignment
- Melting/stacking and Pivoting/unstacking data sets
- Groupby feature allowing split -> apply -> combine operations on data sets
- Data merging and joining
Pandas is also a high performance library, with much of its code written in Cython or C. Unfortunately, Pandas can have a bit of a steep learning curve — In this post, I’ll cover some introductory tips and tricks to help one get started with this excellent package.
- This post was partially inspired by Tom Augspurger’s Pandas tutorial, which has a youtube video that can be viewed along side it. We also suggest some other excellent resource materials — where relevant — below.
- The notebook we use below can be downloaded from our github page. Feel free to grab it and follow along.
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Code, references, and examples of this project are on Github.
In this post, I’ll describe the soup to nuts process of automating a literature search in Pubmed Central using R.
It feels deeply satisfying to sit back and let the code do the dirty work.
Is it as satisfying as a bowl of red-braised beef noodle soup with melt-in-your-mouth tendons from Taipei’s Yong Kang Restaurant (featured image)?
If you have to do a lit search like this more than once, then I have to say the answer is yes — unequivocally, yes.
We recently developed our NBA dashboard in the programming language Processing. In addition, we have Processing apps in our post on classification without negative examples as well as our weekly NBA predictions. Here, we will briefly describe (and recommend) Processing and discuss some tips and tricks we have discovered in developing and deploying our above-mentioned apps to our WordPress blog. (more…)