data-science data-newsletter-5
Scaling Data Teams
09 Oct 2017 Needs of data teams are mostly around data access and sharing; Columnar databases are often more efficient for analytics; MS Excel is useful at many scales; Stakeholder communication is important to make your work more relevant; Use metrics to get to know how data products are being used.
Matplotlib: Pyplot By Example
05 Oct 2017 Examples for common operations on PyPlot, like changing figure size, changing title and tick sizes, changing legends, etc.
Paper Summary: WSABIE: Scaling Up To Large Vocabulary Image Annotation
05 Oct 2017 Summary of the 2011 article "WSABIE: Scaling Up To Large Vocabulary Image Annotation" by Weston et al.
paper-summary tags neural-nets embeddings
Paper Summary: Recursive Neural Language Architecture for Tag Prediction
05 Oct 2017 Summary of the 2016 article "Recursive Neural Language Architecture for Tag Prediction" by Kataria.
Thoughts on App Monetization with Examples from Popular Apps
05 Oct 2017 A couple of thoughts on what approaches seem to work best when optimizing monetization on web/mobile apps. Tips include: Focus on the First Purchase, Mix Free and Paid Features on the same interface, Give away freebies consistently.
Read More ›Parallel For Loops in Python: Examples with Joblib
02 Oct 2017 Joblib.Parallel is a simple way to spread your for loops across multiple cores, for parallel execution.
Read More ›How to Make Gif Animations from Screencasts on Ubuntu
01 Oct 2017 To make short gif-videos on Ubuntu, you can use Kazam for the Screencasts and then Gifify to turn those videos into gif animations.
Read More ›How to Change the Default Application for a given Extension on Ubuntu
01 Oct 2017 Change the default applications used by certain file extensions.
Read More ›embeddings structure paper-summary neural-networks
Paper Summary: Translating Embeddings for Modeling Multi-relational Data
01 Oct 2017 Summary of the 2013 article "Translating Embeddings for Modeling Multi-relational Data" by Bordes et al.
data-science python data-preprocessing
Feature Scaling: Quick Introduction and Examples using Scikit-learn
27 Sep 2017 Feature Scaling techniques (rescaling, standardization, mean normalization, etc) are useful for all sorts of machine learning approaches and critical for things like k-NN, neural networks and anything that uses SGD (stochastic gradient descent), not to mention text processing systems.
Included examples: rescaling, standardization, scaling to unit length, using scikit-learn. Read More ›