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Weekly Selection – Sep 28, 2018

Wikipedia Data Science: Working with the World's Largest Encyclopedia

By William Koehrsen – 17 min read

Wikipedia is one of modern humanity’s most impressive creations. Who would have thought that in just a few years, anonymous contributors working for free could create the greatest source of online knowledge the world has ever seen?


Illustrated Guide to LSTM’s and GRU’s: A step by step explanation

By Michael Nguyen – 10 min read

In this post, we’ll start with the intuition behind LSTM ‘s and GRU’s. Then I’ll explain the internal mechanisms that allow LSTM’s and GRU’s to perform so well. If you want to understand what’s happening under the hood for these two networks, then this post is for you.


Neural Networks to Predict the Market

By Vivek Palaniappan – 8 min read

Machine Learning and deep learning have become new and effective strategies commonly used by quantitative hedge funds to maximize their profits. As an AI and finance enthusiast myself, this is exciting news as it combines two of my areas of interest.


5 Reasons why Businesses Struggle to Adopt Deep Learning

By Ganes Kesari – 5 min read

So, you’ve heard the dazzling sales pitch on deep learning and are wondering whether it actually works in production. The top question companies have is on whether the promised land of perennial business benefits is a reality.


How to rapidly test dozens of deep learning models in Python

By Thomas Ciha – 5 min read

Optimizing machine learning (ML) models is not an exact science. The best model architecture, optimization algorithm and hyperparameter settings depend on the data you’re working with.


Convolutional Neural Networks for Beginners: Practical Guide with Python and Keras

By Jordi TORRES.AI – 20 min read

At this point, we are ready to deal with another type of neural networks, the so-called convolutional neuronal networks, widely used in computer vision tasks. These networks are composed of an input layer, an output layer and several hidden layers, some of which are convolutional, hence its name.


Multi-Class Text Classification Model Comparison and Selection

By Susan Li – 7 min read

When working on a supervised machine learning problem with a given data set, we try different algorithms and techniques to search for models to produce general hypotheses, which then make the most accurate predictions possible about future instances.


Here’s how you can get a 2–6x speed-up on your data pre-processing with Python

By George Seif – 5 min read

Python is the go-to programming language for all things machine learning. It’s easy to use and has many fantastic libraries that make crunching data a breeze! But things get a big trickier when we’re dealing with lots of data…


Why do I Call Myself a Data Scientist?

By Favio Vázquez – 7 min read

Claimed as the "sexiest job of the 21st Century" here I’ll discuss the reasons for my proclamation as a Data Scientist, beyond the hype.


See Robot Play: an exploration of curiosity in humans and machines

By Norman Di Palo – 9 min read

From a survival point of view, the main biological needs that drive animals and humans are not particularly different. Humans and animals need to eat and drink in order to survive, take shelter, and they feel an impulse to reproduce in order to keep the species alive. But, as it is evident, the behavior of humans and animals differ completely.


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