Data Science Weekly Newsletter - Issue 166

Issue #166

Jan 26 2017

Editor Picks
 
  • Why Deep Learning Needs Assembler Hackers
    When I first started looking at the engineering side of neural networks, I assumed that I’d be following the path I’d taken on the rest of my career and getting most of my performance wins from improving the algorithms, writing clean code, and generally getting out of the way so the compiler could do its job of optimizing it. Instead I spend a large amount of my time worrying about instruction dependencies and all the other hardware details that we were supposed to be able to escape in the 21st century. Why is this?...
  • How to Get a Job In Deep Learning
    Getting involved in deep learning may seem a bit daunting at first, but the good news is that there are more resources out there now than ever before. (There’s also a huge, pent up demand for engineers who know how to implement deep learning in software.) So, if you want to get yourself a job in deep learning but need to get yourself up to speed first, let this be your guide!...
  • food2vec - Augmented cooking with machine intelligence
    Building a recommendation system for food & exploring the world's cuisines. Check out the tools demo to explore food analogies and recommendations, or scroll down for an interactive map of a hundred thousand recipes from around the world...
 
 

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

 
  • What is a GPU and Why Do I Care? A Businessperson's Guide
    While 2016 was the year of the GPU for a number of reasons, the truth of the matter is that outside of some core disciplines (deep learning, virtual reality, autonomous vehicles) the reasons why you would use GPUs for general purpose computing applications remain somewhat unclear. As a company whose products are tuned for this exceptional compute platform, we have a tendency to assume familiarity, often incorrectly. Our New Year’s resolution is to explain, in language designed for business leaders, what a GPU is and why you should care...
  • Understanding How Machines Learn, Through Prototyping
    This is the second article in a larger series exploring the intersection of design and existing artificial intelligence technology through experiments, prototypes and concepts. We believe this is a critically important topic for the design community and beyond, so we’re sharing what we learn along the way...
  • The Rise of the Data Engineer
    I joined Facebook in 2011 as a business intelligence engineer and by the time I left in 2013, I was a data engineer. I was not promoted or assigned a new role, we simply came to realize that the work we were doing was transcending classic business intelligence and that the role we had created for ourselves was a new discipline. As my team was at forefront of this transformation, we were developing new skills, new ways of doing things, new tools, and more often than not turning my back to traditional methods. We were pioneers. We were data engineers!...
  • Attention Transfer
    PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer"...
  • Deep Network Guided Proof Search
    Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search...
  • One Dataset, Visualized 25 Ways
    “Let the data speak.” It’s a common saying for chart design. The premise — strip out the bits that don’t help patterns in your data emerge — is fine, but people often misinterpret the mantra to mean that they should make a stripped down chart and let the data take it from there. To show you what I mean, I present you with twenty-five charts below...
 
 

Jobs

 
  • Data Scientist (M/F) - Deutsche Post DHL Group - Bonn, Germany

    Do you want to contribute to making the leading global logistics company more data-driven? If so, join our growing central Data Analytics Team of Deutsche Post DHL Group. We work with our operative departments to solve concrete business problems by using mathematical methods, such as time series analysis, Operations Research methods and machine learning. You will build analytical models to answer a wide variety of questions. For example, you will predict daily shipment volumes for accurate operational capacity planning, and you will handle the real-time optimization of costs and utilization of our global transport network, which includes more than 200 countries.

    You should have a deep knowledge of statistics and/or mathematical optimization, be skilled in scripting languages (Python, R) and be experienced in SQL. We are looking for energetic and success-oriented problem solvers who are comfortable explaining complex analytical and technical content to various audiences....
 
 

Training & Resources

 
  • What's Functional Programming All About?
    There are many descriptions floating around the internet, trying to explain functional programming in simple terms. Unfortunately, most discuss details only loosely related to functional programming, while others focus on topics that are completely irrelevant. So of course, I had to write my own!...
  • Why use SVM?
    Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. All code is available on Github...
 
 

Books

 

  • Algorithms for Data Science

    "This groundbreaking textbook on practical data analytics unites fundamental principles, algorithms, and data. Programming fluency and experience with real and challenging data sets are gained through more than 20 Python and R tutorials and lots of exercises with solutions."...


    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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