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 ›python data-visualization data-newsletter-5
Matplotlib, Pyplot, Pylab etc: What's the difference between these and when to use each?
26 Sep 2017 Do you often get confused with terms like maptlotlib, pyplot, pylab, figures, axes, gcf, gca, etc and wonder what they mean? Matplotlib is the toolkit, PyPlot is an interactive way to use Matplotlib and PyLab is the same thing as PyPlot but with some extra shortcuts.
data-newsletter-5 data-science best-practices
5 Tips for moving your Data Science Operation to the next Level
26 Sep 2017 Principles for disciplined data science include: Discoverability, Automation, Collaboration, Empowerment and Deployment.
recommender-systems data-newsletter-5
Highlights of the Talk with Dr. Konstan on Recommender Systems
24 Sep 2017 Some highlights of the Podcast Episode with Dr. Joseph Konstan on interesting topics related to Recommender Systems. Discussed topics include serendipity, serpentining, diversity and temporal effects.
python data-visualization plotting
Seaborn by Example: Data Visualization and Plotting using Python
09 Sep 2017 Seaborn is a higher-level interface to Matplotlib. It has a more convenient API and has useful data visualization functions right out of the box.
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