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Weekly Selection – Jun 29, 2018

How Bayesian statistics convinced me to hit the gym

By Tuan Doan Nguyen – 10 min read

So a little bit of background information: I originally come from Vietnam, went to high school in Singapore and currently attend college in USA. I often heard people making fun of how small I look, how underweight I must be and how I should do exercise, hit the gym and gain weight in order to have "a nicer physique".


Getting started with reading Deep Learning Research papers: The Why and the How

By Nityesh Agarwal – 8 min read

How do you continue the learning after you have consumed that book or completed that amazing online course on Deep Learning? How do you become "self-sufficient" so that you don’t have to rely on someone else to break down the latest breakthrough in the field?


An Introductory Example of Bayesian Optimization in Python with Hyperopt

By William Koehrsen – 9 min read

Although finding the minimum of a function might seem mundane, it’s a critical problem that extends to many domains. For example, optimizing the hyperparameters of a machine learning model is just a minimization problem: it means searching for the hyperparameters with the lowest validation loss.


Deep Learning on the Edge

By Bharath Raj – 7 min read

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases.


The 10 coolest papers from CVPR 2018

By George Seif – 8 min read

The 2018 Conference on Computer Vision and Pattern Recognition (CVPR) took place last week in Salt Lake City, USA. It’s the world’s top conference in the field of computer vision. This year, CVPR received 3,300 main conference paper submissions and accepted 979. Over 6,500 attended the conference and boy was it epic!


The Data Science Bubble

By Daniel Carroll – 4 min read

I’m afraid I have some bad news for my fellow data scientists; data science is one of the worst investments a company can make today. While individual projects can do 1000x returns on investment, the chances of your companies next project doing that are next to none, and in fact the chances of it failing completely are quite high. Last year Gartner estimated that as many as 85% of data initiatives fail.


Data Science for Startups: Deep Learning

By Ben Weber – 13 min read

This blog post is a brief introduction to using the Keras deep learning framework to solve classic (shallow) machine learning problems. It presents a case study from my experience at Windfall Data, where I worked on a model to predict housing prices for hundreds of millions of properties in the US.


Towards Rapid Discovery of Viable Pipelines

By Sean McClure – 22 min

A tenet of software development is to automate as much as possible. This is the DevOps mindset, where an effort towards automation helps teams push features continuously while ensuring best-practices are respected.


Analyse a Soccer game using Tensorflow Object Detection and OpenCV

By Priya Dwivedi – 4 min read

For the data scientist within you lets use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips. See example gif below of the game b/w Australia and Peru played where we can identify all the players + referees, the soccer ball and also predict which team the player is based on the color of their jersey. And all of this can be done real time.


Winning the War Against Imbalanced Data

By Rafael Pierre – 9 min read

For years, fraudsters would simply take numbers from credit or debit cards and print them onto blank plastic cards to use at brick-and-mortar stores. But in 2015, Visa and Mastercard mandated that banks and merchants introduce EMV – chip card technology, which made it possible for merchants to start requesting a PIN for each transaction.


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