Data Science Weekly Newsletter - Issue 147

Issue #147

Sept 15 2016

Editor Picks
 
  • Why does deep and cheap learning work so well?
    We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through "cheap learning" with exponentially fewer parameters than generic ones, because they have simplifying properties tracing back to the laws of physics...
  • Human in AI Loop
    A.I is out to get us. It will replace us all.. or maybe, it’s just another tool that we will get used to like calculators or iPhones...
  • Machine Learning in a Year
    From being a total ml noob to start using it at work. This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject...
 
 

A Message from this week's Sponsor:

 

  • Harness the business power of big data.

    How far could you go with the right experience and education? Find out. At Capitol Technology University. Earn your PhD Management & Decision Sciences — in as little as three years — in convenient online classes. Banking, healthcare, energy and business all rely on insightful analysis. And business analytics spending will grow to $89.6 billion in 2018. This is a tremendous opportunity — and Capitol’s PhD program will prepare you for it. Learn more now!
 
 

Data Science Articles & Videos

 
  • Deep Learning Isn't a Dangerous Magic Genie. It's Just Math
    Deep Learning rapidly ‘eating’ artificial intelligence. But let’s not mistake this ascendant form of artificial intelligence for anything more than it really is. The famous author Arthur C. Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” And deep learning is certainly an advanced technology—it can identify objects and faces in photos, recognize spoken words, translate from one language to another, and even beat the top humans at the ancient game of Go. But it’s far from magic...
  • Generative Visual Manipulation on the Natural Image Manifold
    Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable artistic skill, it is easy to "fall off" the manifold of natural images while editing. In this paper, we propose to learn the natural image manifold directly from data using a generative adversarial neural network...
  • Plenary Panel: Is Deep Learning the New 42?
    According to Douglas Adams’s famous “Hitchhiker’s Guide to the Galaxy” after 7.5 millions years of work the “Deep Thought” computer categorically found out that 42 is the “Answer to the Ultimate Question of Life, the Universe, and Everything” (although unfortunately, no one knows exactly what that question was). Rather than wait another 7.5 million years for “Deep Thought” to answer our quest we have assembled a distinguished panel of experts to give us their opinion on deep learning and its present and future impact....
  • Running a Deep Learning (Dream) Machine
    I’m used to working in the cloud and will keep doing so for production-oriented systems/algorithms. There are however huge drawbacks to cloud-based systems for more research oriented tasks where you mainly want to try out various algorithms and architectures, to iterate and move fast. To make this possible I decided to custom design and build my own system specifically tailored for Deep Learning, stacked full with GPUs. This turned out both more easy and more difficult than I imagined. In what follows I will share my “adventure” with you. I hope it will be useful for both novel and established Deep Learning practitioners...
  • Measles Incidence in Altair
    This is an example of reproducing the Wall Street Journal's famous Measles Incidence Plot in Python using Altair...
 
 

Jobs

 
  • Head of FCO Data Science - Foreign & Commonwealth Office - London

    The Foreign & Commonwealth Office (FCO) promotes the United Kingdom's interests overseas, supporting our citizens and businesses around the globe.

    As Head of FCO Data Science, it’ll be down to you to establish the FCO’s new data science capability. Leading the team, you’ll:
    • Advise senior stakeholders on the sound use of data
    • Provide insight to improve policy making
    • Evaluate the impact of Her Majesty’s Government (HMG) interventions
    • Help predict future instability, threats and opportunity for prosperity

    This is a role at the cutting edge of foreign policy and a truly unique opportunity to make a real impact on the world. To join us, you’ll need:
    • An analytical Master’s degree
    • Substantial data science experience – including practice in Python or R
    • An understanding of Regression Analysis, Natural Language Processing and Sentiment Analysis

    To apply please visit: www.capitaras.co.uk/fco.

    Closing date: September 26th 2016
 
 

Training & Resources

 
  • The Neural Network Zoo
    With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures....
  • NakedTensor
    Simplest example of Machine Learning in TensorFlow...
  • Scikit-Optimize
    Scikit-Optimize, or skopt, is a simple and efficient library for sequential model-based optimization, accessible to everybody and reusable in various contexts. The library is built on top of NumPy, SciPy and Scikit-Learn...
 
 

Books

 

  • Test-Driven Machine Learning

    The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression...


    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
 
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