Data Science Weekly Newsletter

Issue

172

March 9, 2017

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Editor's Picks

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Why is machine learning 'hard'?
The difficulty is that machine learning is a fundamentally hard debugging problem. Debugging for machine learning happens in two cases: 1) your algorithm doesn't work or 2) your algorithm doesn't work well enough. What is unique about machine learning is that it is ‘exponentially’ harder to figure out what is wrong when things don’t work as expected...

Machine-Learning Algorithm Predicts Laboratory Earthquakes
The breakthrough has astonished geologists and raises the possibility that real earthquake prediction could be next...

Josef: A robot who tries to learn how to draw
Josef is feedforward neural network based on synapticjs and distant relative of Istanbul's famous robot poet Deniz Yilmaz. The network tries to predict next action of the drawing grammar. Works like an Lindenmayer interpreter running by a neural network which is a non-deterministic way to evaluate that kind of self-rewriting systems...

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Introducing Similarity Search at Flickr
Flickr nearest neighbor similarity search: If you hover over a photo on a search result page, you will reveal a “…” button that exposes a menu that gives you the option to search for photos similar to the photo you are currently viewing...

Assisting Pathologists in Detecting Cancer with Deep Learning
To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow...

Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image
Guessing the location of a randomly chosen Street View image is hard, even for well-traveled humans. But Google’s latest artificial-intelligence machine manages it with relative ease...

Data Over Matter — Innovating the Next Generation of Data Products
In this talk, you’ll learn about data science at Netflix. Specifically: Data infrastructure and tools at Netflix; A/B testing at Netflix; Netflix’s culture of innovation...

Some Reflections on Being Turned Down for a Lot of Data Science Jobs
In the last five years, I've clearly interviewed for a lot of data science jobs, and I've also been turned down for a lot of data science jobs. I've spent a good bit of time reflecting on why I wasn't offered this job or that. Several folks have asked me if I had any advice to share on the experience, and I hope to offer that here...

The Meta Model and Meta Meta-Model of Deep Learning
The model for deep learning consists of a computational graph that are most conveniently constructed by composing layers with other layers. Most introductory texts emphasize the individual neuron, but in practice it is the collective behavior of a layer of neurons that is important. So from an abstraction perspective, the layer is the right level to think about...

Triple Pendulum CHAOS!
Earlier this week a tweet made the rounds which features a video that nicely demonstrates chaotic dynamical systems in action. Naturally, I immediately wondered whether I could reproduce this simlulation in Python. This post is the result...

Stopping GAN Violence: Generative Unadversarial Networks
While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs)...

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Data Scientist - Hulu - Santa Monica, CA
We are looking for data scientists who are passionate about using data to drive strategy and product recommendations, You will be engaged with senior leaders to design well-constructed analyses and work cross-functionally with analysts, product managers and engineers to effectively deliver actionable results. You will work on variety of domains such as data science, machine learning and optimization; lead cutting edge analytical solution development pipeline and contribute to external research via attending conferences and collaborations...

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IPython Or Jupyter?
Today’s blog post intends to illustrate some of the core differences between the two more explicitly, not only starting from the origins of both to explain how the two relate, but also covering some specific features that are either part of one or the other, so that it will be easier for you to make the distinction between the two!...

A Simple Guide for Python Packaging
This is a simple instruction on how to go from nothing to a package that you can proudly put it in your portfolio to be used by other people...

Fitting Gaussian Process Models in Python
Using scikit-learn, GPflow and PyMC3 to build Gaussian process models...

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Data Scientists at Work
"A collection of interviews with 16 of the world's most influential and innovative data scientists from across the spectrum of this hot new profession - from Yann LeCun at Facebook to Jake Porway at DataKind"...

For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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