monetization

Thoughts on App Monetization with Examples from Popular Apps

04 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.

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python parallel

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.

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ubuntu animations

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.

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ubuntu

How to Change the Default Application for a given Extension on Ubuntu

01 Oct 2017   Change the default applications used by certain file extensions.

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embeddings structure paper-summary neural-networks

Paper Summary: Translating Embeddings for Modeling Multi-relational Data

30 Sep 2017   Summary of the 2013 article "Translating Embeddings for Modeling Multi-relational Data" by Bordes et al.

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data-science python data-preprocessing

Feature Scaling: Quick Introduction and Examples using Scikit-learn

26 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.

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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.

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data-newsletter-5 data-science best-practices

5 Tips for moving your Data Science Operation to the next Level

25 Sep 2017   Principles for disciplined data science include: Discoverability, Automation, Collaboration, Empowerment and Deployment.

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technology   data-science machine-learning svm

SVM and Kernels: The very least every data scientist needs to know

25 Sep 2017   Quick recap with main points on SVMs, where and how to use them and basic exmaples.

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recommender-systems data-newsletter-5

Highlights of the Talk with Dr. Konstan on Recommender Systems

23 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.

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