Data Science Weekly Newsletter - Issue 209

Issue #209

Nov 23 2017

Editor Picks
 
  • Fly brain inspires computing algorithm
    Flies use an algorithmic neuronal strategy to sense and categorize odors. Dasgupta et al. applied insights from the fly system to come up with a solution to a computer science problem. On the basis of the algorithm that flies use to tag an odor and categorize similar ones, the authors generated a new solution to the nearest-neighbor search problem that underlies tasks such as searching for similar images on the web...
  • This AI Can Spot Art Forgeries by Looking at One Brushstroke
    Detecting art forgeries is hard and expensive. Art historians might bring a suspect work into a lab for infrared spectroscopy, radiometric dating, gas chromatography, or a combination of such tests. AI, it turns out, doesn’t need all that: it can spot a fake just by looking at the strokes used to compose a piece...
 
 

A Message from this week's Sponsor:

 

 
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SherlockML is a fully managed cloud platform for Data Science. Join hundreds of Data Scientists using it every day to explore data and deploy AI models. SherlockML supports Python, R Studio, and Apache Zeppelin and comes pre-installed with hundreds of packages such as Pandas, NumPy and TensorFlow. For ultimate flexibility there is a command line interface. Turbocharge your Data Science and sign up for a free trial here
 
 
 

Data Science Articles & Videos

 
  • This AI Learns Your Fashion Sense and Invents Your Next Outfit
    Artificial intelligence might just spawn a whole new style trend: call it “predictive fashion.” ... researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person’s style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends...
  • Improving TripAdvisor Photo Selection With Deep Learning
    The newly redesigned TripAdvisor.com emphasizes traveler photos throughout the site, but not all of these photos are useful in every situation. Deep Learning networks provide an excellent opportunity for us to improve our users’ experience by highlighting the most attractive and useful photos for varying presentation contexts. This post will discuss our approach for gathering training data, developing a model, and scaling it up to over 110 million photos and 7 million places of interest...
  • Our NIPS 2017: Learning to Run approach
    For 3 months, from July to 13 November (sometimes with long breaks though), me and my friend Piotr Jarosik participated in the NIPS 2017: Learning to Run competition. In this post we describe how it went. We release the full source code...
  • An On-device Deep Neural Network for Face Detection
    Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. This article discusses these challenges and describes the face detection algorithm...
  • “Ok, Google — How do you run Deep Learning Inference on Android Using TensorFlow?”
    During my time at Insight, I deployed a pretrained WaveNet model on Android using TensorFlow. My goal was to explore the engineering challenge of bringing deep learning models onto devices and making things work! In this post, I’ll quickly walk you through the process of building a general speech-to-text recognition application on Android with TensorFlow. I hope after this post you’ll be able to build your own DL-powered applications next time!...
  • Deep Learning: A Bayesian Perspective
    Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning...
  • Evolving Stable Strategies
    In the previous article, I have described a few evolution strategies (ES) algorithms that can optimise the parameters of a function without the need to explicitly calculate gradients. These algorithms can be applied to reinforcement learning (RL) problems to help find a suitable set of model parameters for a neural network agent. In this article, I will explore applying ES to some of these RL problems, and also highlight methods we can use to find policies that are more stable and robust...
 
 

Jobs

 
  • Intern, Data Science - Foot Locker Inc - NYC

    As an Intern you will learn about the retail and ecommerce industry; participate in a collaborative business project, volunteer opportunities, corporate training. Be a part of the team that leads innovation in the athletic ecommerce industry and wear cool kicks to work every day!

    Collaborate with associates on various levels and across functions to discover insights, causalities, create predictive models, machine-learning algorithms and other transformational initiatives Gain familiarity with tools and techniques, such as: exploratory data discoveries, data science research, data mining, building data products...
 
 

Training & Resources

   
 

Books

 

  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century

    An insightful, revealing history of how mathematics transformed our world...

    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."


    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
P.S., Want to reach our audience / fellow readers? Consider sponsoring. We just opened up booking for 2018 - grab a spot now; first come first served! Email us for more details - All the best, Hannah & Sebastian
 
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