• The Care and Feeding of Data Scientists:
  How to Build, Manage, and Retain a Data Science Team
  More and more people find themselves managing data scientists without the necessary toolset or role mod‐ els or mentorship to do the job well. This report aims to fill that gap—to become a resource that data sci‐ ence leaders (whether they’re data scientists or engineers or product managers) can use to understand how data science management is both similar to, and distinct from, other types of management and to learn concrete tips for building and sustaining their teams...

• The Long Game of Research
  Moshe Vardi reminds us that research is a long game, using the ebbs and flows of neural net history as a case study...

• Why software projects take longer than you think – a statistical model
  Anyone who built software for a while knows that estimating how long something is going to take is hard. It’s hard to come up with an unbiased estimate of how long something will take, when fundamentally the work in itself is about solving something. One pet theory I’ve had for a really long time, is that some of this is really just a statistical artifact...

Domino Data Lab

Now free to try: Domino Data Lab, the data science platform used by teams at Allstate, Red Hat, Vevo, Zurich, Instacart, Gap, Bayer, Dell, Postmates, and many others.
Data scientists use it to do more model development and less DevOps, to elevate their research through collaboration and reproducibility, and to deliver value to the business faster through model deployment. It works with the languages, packages, notebooks, and IDEs they already love.
Get started for free
and enjoy free access to 2x 8GB of RAM to run your models.

• Generating High-Resolution Fashion Model Images Wearing Custom Outfits
  Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determined set of outfits. In this paper, we broaden these visualizations by generating high-resolution images of fashion models wearing a custom outfit under an input body pose. We show that our approach can not only transfer the style and the pose of one generated outfit to another, but also create realistic images of human bodies and garments....

• Facebook is creating an AI assistant for Minecraft
  The popular video game provides the best environment for an AI to learn a wide range of tasks, the company’s researchers say....

• Top NLP Research Papers with Business Applications from ACL 2019
  This year’s annual meeting of the Association for Computational Linguistics (ACL 2019) was bigger than ever. It is becoming more and more challenging to keep track of the latest research advances in your area with such an overwhelming number of good research papers coming out. So, for your convenience, we’ve picked up and summarized several interesting research papers that might have particularly useful applications in a business setting...

• Science at Uber: Applying Artificial Intelligence at Uber
  Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users...

• Taking Constrained ML to the Next Level
  Squeezing keyword spotting into 16KB with ~91% accuracy using Kronecker decomposition...

• Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
  We now released code and trained models for our GSCNN semantic segmentation work done at Nvidia AI...

• A Probabilistic Representation of Deep Learning
  In this work, we introduce a novel probabilistic representation of deep learning, which provides an explicit explanation for the Deep Neural Networks (DNNs) in three aspects: (i) neurons define the energy of a Gibbs distribution; (ii) the hidden layers of DNNs formulate Gibbs distributions; and (iii) the whole architecture of DNNs can be interpreted as a Bayesian neural network...

• Weight Agnostic Neural Networks
  In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training...

• What Sections to include (and not) on your Data Science Resume
  Wondering exactly what should be on your Data Science Resume? Spending a lot of time scouring resume advice sites but not finding anything specific to Data Science? It can be hard (not to mention time consuming and frustrating), trying to figure out what Sections should be on your resume...

Become a Data Analyst. Job Guaranteed.

The demand for qualified data analysts is high. That’s why Springboard launched the Data Analytics Career Track, a comprehensive bootcamp that will equip you to transition into a role as a data analyst. To gain the skills, you’ll work through projects that will allow you to bring all of your skills together, and become the center of a powerful portfolio. And you’ll do it all with the guidance of your personal mentor and career coach.
*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!

• A Beginner's Guide to the Mathematics of Neural Networks
  In this paper I try to describe both the role of mathematics in shaping our understanding of how neural networks operate, and the curious new mathematical concepts generated by our attempts to capture neural networks in equations. My target reader being the non-expert, I will present a biased selection of relatively simple examples of neural network tasks, models and calculations, rather than try to give a full encyclopedic review-like account of the many mathematical developments in this field....

• Targeted Dropout
  Finding efficient subnetworks in over-parameterized models...

• New LILA dataset: Chesapeake Land Cover.
  High-resolution aerial imagery, low-resolution multi-spectral imagery, & high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping...

• Python Crash Course: A Hands-On, Project-Based Introduction to Programming Thorough introduction to programming with Python...
  "I have read multiple beginner guides to Python. I am currently up to chapter 11 in Python Crash Course. So far this is far and away my favorite Python programming book..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
  .

  ---

   
  P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian