Data Science Weekly Newsletter - Issue 58

Issue #58

Jan 1 2015

 

Editor Picks

 
  • 4 Times Data Science Saved the Day
    Increasingly, 21st century insight and innovation are powered by data. As data science matures past its infancy, it’s become an elemental part of every industry from sports and retail to health and finance...
  • Why is it so hard to know if changing coaches has any effect?
    Most of the people who have asked "how much will changing the coach help the team" have found the answer to be somewhere between "a little" and "it won't." Yet, it seems so incredibly obvious that some coaches are bad and getting rid of them will help the team improve. So why can't we demonstrate that this is true?...
  • Julia Is Awesome, But...
    Here’s a language that gives near-C performance that feels like Python or Ruby with optional type annotations (that you can feed to one of two static analysis tools) that has good support for macros plus decent-ish support for FP, plus a lot more. What’s not to like?...
 
 

Data Science Articles & Videos

 
  • FitNets: Hints for Thin Deep Nets
    The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student...
  • Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
    We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolution implementations: one based on NVIDIA's cuFFT library, and another based on a Facebook authored FFT implementation, fbfft, that provides significant speedups over cuFFT (over 1.5x) for whole CNNs...
  • Django Development Mistakes In 2014
    With 2015 rapidly approaching, I took some time to think about what I would have done differently from a development perspective in 2014. In my previous article, 11 Things I wish I knew about Django Before Starting My Company, I started the list. Now it’s time to add to it!...
  • Machine Intelligence Cracks Genetic Controls
    Every cell in your body reads the same genome, the DNA-encoded instruction set that builds proteins. But your cells couldn’t be more different. Neurons send electrical messages, liver cells break down chemicals, muscle cells move the body. How do cells employ the same basic set of genetic instructions to carry out their own specialized tasks? The answer lies in a complex, multilayered system that controls how proteins are made...
  • Building Language Detector via Scikit-Learn
    Building language detection or identification is hard if you want to do keyword search in the text based on dictionaries in the language. Yet with data and machine learning, I could build a relatively good language detector...
  • 2014 in Computing: Breakthroughs in Artificial Intelligence
    The holy grail of artificial intelligence—creating software that comes close to mimicking human intelligence—remains far off. But 2014 saw major strides in machine learning software that can gain abilities from experience. Companies in sectors from biotech to computing turned to these new techniques to solve tough problems or develop new products....
 
 

Jobs

 
  • Data Scientist - BMW Technology Office, Chicago

    The BMW Group is committed to developing creative, breakthrough connected car services that integrate consumers’ digital lives with their mobility needs. The Data Scientist is responsible for developing algorithms and building predictive models to solve business problems and help enhance or create new products and digital services. This position must distill output from models into insights that lead to new or improved products. He or she must be an effective communicator of learned insights to all levels of the business. Lastly, the incumbent must engage with and understand other functions within the group to help build models, hypotheses and priorities...
 
 

Training & Resources

 
  • Julia by Example
    Julia is a high-level, high-performance dynamic programming language for technical computing. This site is a non official series of examples of Julia...
  • Deep Learning Reading List
    Following is a growing list of some of the materials i found on the web for Deep Learning beginners...
  • LamdaNet
    Purely functional artificial neural network library implemented in Haskell...
 
 

Books

 

  • Practical Data Science with R

    Explanation of basic principles with real use cases ...

    "A well rounded, occasionally high-level introductory text that will leave you feeling prepared to participate in the Data Science conversation at work, from earliest planning to presentation and maintenance..."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
P.S. Happy New Year to all! Wishing you a wonderful 2015 :)
- All the best, Hannah & Sebastian
 
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