Roundup 11/8/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Plus, a few leftovers from Friday and the weekend.

In Harvard Business Review, SAP’s Maxwell Wessel argues that you don’t need Big Data, which tells me that SAP lacks a solution for Big Data.

Jeff Catlin of Lexalytics explains why VCs are throwing heaps of money at machine learning.

The Financial Stability Board announces an initiative to understand the applications of machine learning in financial services.

Google DeepMind and Blizzard Entertainment announce a partnership to release StarCraft II as an AI research environment. Storylanche ensues.

Smart light bulbs are a thing. Rule 41: if a thing is connected, it is hackable.

Methods and Techniques

— Researchers at Google DeepMind tweak a deep learning algorithm so it can recognize images and other things from a single example.

— Jason Brownlee explains how to implement a backpropagation algorithm in Python. This article is part of a series on his Machine Learning Master site. Other recent explainers include articles on implementing learning vector quantization, perceptrons, logistic regression with stochastic gradient descent and linear regression with stochastic gradient descent.


— Something from last week that I missed. On the Hortonworks blog, Syed Mahmood and Vinay Shukla offer five predictions about Apache Spark.

— In a webinar, Intel delivers an overview of its software accelerators for deep learning, including the Math Kernel Library, the Data Analytics Acceleration Library, and the Deep Learning SDK. Slides here.

— Microsoft offers free test drives for its data science virtual machine.

— In a Kaggle forum, Ben Gorman describes mltools, an R package for machine learning.


— Toshiba announces the development of a Time Domain Neural Network that uses an extremely low power consumption neuromorphic semiconductor circuit for deep learning. The technology has the potential to bring deep learning to edge devices, such as sensors and smartphones.


— Researchers from the Swiss Federal Institute in Zurich use ML to measure gender bias in astronomy. First, they build a model to predict citations based on characteristics of a paper (without considering the gender of the lead author.) Then, they used their model to determine the expected number of citations for a set of papers with female lead authors.

— James Anderson summarizes a study that machine learning may be able to diagnose autism from genetic data.

— Combatant Gentlemen, a fashion startup, uses machine learning to predict what shoppers will buy.

— Research and Markets releases Credit Pulse, a tool that analyzes SEC filings with machine learning and generates a risk rating for companies within one minute of the filing.

— NVIDIA saves zebras with deep learning.

— Deep learning powers unmanned aerial vehicles (aka drones.)

— On the Gamasutra blog, Anders Drachen explains a technique for user retention with machine learning.

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