Data Science Weekly Newsletter - Issue 217

Issue #217

Jan 18 2018

Editor Picks
 
  • Turning Design Mockups Into Code With Deep Learning
    Currently, the largest barrier to automating front-end development is computing power. However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now. In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup...
 
 

A Message from this week's Sponsor:

 

 
Quick Question For You: Do you want a Data Science job?

After helping hundred of readers like you get Data Science jobs, we've distilled all the real-world-tested advice into a self-directed course.

The course is broken down into three guides:
  1. Data Science Getting Started Guide. This guide shows you how to figure out the knowledge gaps that MUST be closed in order for you to become a data scientist quickly and effectively (as well as the ones you can ignore)

  2. Data Science Project Portfolio Guide. This guide teaches you how to start, structure, and develop your data science portfolio with the right goals and direction so that you are a hiring manager's dream candidate

  3. Data Science Resume Guide. This guide shows how to make your resume promote your best parts, what to leave out, how to tailor it to each job you want, as well as how to make your cover letter so good it can't be ignored!

Click here to learn more ...
   
 

Data Science Articles & Videos

 
  • Big Bets on A.I. Open a New Frontier for Chip Start-Ups, Too
    Today, at least 45 start-ups are working on chips that can power tasks like speech and self-driving cars, and at least five of them have raised more than $100 million from investors. Venture capitalists invested more than $1.5 billion in chip start-ups last year, nearly doubling the investments made two years ago, according to the research firm CB Insights...
  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
    We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy...
  • Why the world only has two words for tea
    With a few minor exceptions, there are really only two ways to say “tea” in the world. Both versions come from China. How they spread around the world offers a clear picture of how globalization worked before “globalization” was a term anybody used...
  • Learning Tree-based Deep Model for Recommender Systems
    We propose a novel recommendation method based on tree. With user behavior data, the tree based model can capture user interests from coarse to fine, by traversing nodes top down and make decisions whether to pick up each node to user. Compared to traditional model-based methods like matrix factorization (MF), our tree based model does not have to fetch and estimate each item in the entire set. The experimental results in both open dataset and Taobao display advertising dataset indicate that the proposed method outperforms existing methods...
  • Optimizing Mobile Deep Learning on ARM GPU with TVM
    With the great success of deep learning, the demand for deploying deep neural networks to mobile devices is growing rapidly. Similar to what we do in desktop platforms, utilizing GPU in mobile devices can benefit both inference speed and energy efficiency. However, most existing deep learning frameworks do not support mobile GPU very well...
  • Cloud AutoML: Making AI accessible to every business
    To make AI accessible to every business, we’re introducing Cloud AutoML. Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of....
  • Encrypt your Machine Learning:
    How Practical is Homomorphic Encryption for Machine Learning?

    We have a pretty good understanding of the application of machine learning and cryptography as a security concept, but when it comes to combining the two, things become a bit nebulous and we enter fairly untraveled wilderness. While Fully Homomorphic Encryption is nothing new, we have not seen any practical and efficient applications so far. Recently, we spent time looking into homomorphic encryption to evaluate if it is suitable for tackling some of our privacy and security related concerns...

 

Jobs

 
  • Data Scientist - Lego - London
    Data Scientists create analytics to contribute to the solution of business problems. This involves being able to interpret and deliver the results of their findings to other data scientists and data engineers, by visualization techniques, building Advanced Analytics apps, and narrating interesting stories that stakeholders can relate to. Are you able to do that? Then apply!
 
 

Training & Resources

 
  • Add Layers To A Neural Network In TensorFlow
    This screencast and transcript explain how to add Multiple Layers to a Neural Network in TensorFlow by working through an example where you add multiple ReLU layers and one convolutional layer...
  • Machine Learning Trick of the Day (7): Density Ratio Trick
    In their own ways, all machine learning tricks help us make better probabilistic comparisons. Comparison is the theme of this post—not discussed in this series before—and the right start to this second sprint of machine learning tricks...
 
 

Books

 

  • Seven Databases in Seven Weeks:
    A Guide to Modern Databases and the NoSQL Movement


    "A book that tries to cover multiple database is a risky endeavor, a book that also provides hands on on each is even riskier but if implemented well leads to a great package. I loved the specific exercises the authors covered. A must read for all big data architects who don’t shy away from coding..."


    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 - grab a spot now; first come first served! All the best, Hannah & Sebastian
 
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