Data Science Weekly Newsletter - Issue 305

Issue #305

Sept 26 2019

Editor Picks
 
  • Deep Learning for Symbolic Mathematics
    Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations....

 

A Message from this week's Sponsor:

 

 
SQL, Python, and R. All in One Platform. Free Forever

Mode Studio combines a SQL editor, Python & R notebooks, and visualization builder in one platform. Connect data from anywhere and analyze with your preferred language. Layer custom visualizations (HTML, CSS, or D3.js) or use out-of-the-box charts.

Sign Up - Free Forever

 
 

Data Science Articles & Videos

 
  • Fiddler raises $10.2 million for AI that explains its reasoning
    Explainable AI, which refers to techniques that attempt to bring transparency to traditionally opaque AI models and their predictions, is a burgeoning subfield of machine learning research. It’s no wonder — models sometimes learn undesirable tricks to accomplish goals on training data, or they develop biases with the potential to cause harm if left unaddressed. That’s why Krishna Gade and Amit Paka founded Fiddler...
  • Distributed Machine Learning on Mobile Devices: A Survey
    We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications...
  • Google has released a giant database of deepfakes to help fight deepfakes
    On Tuesday, Google released an open-source database containing 3,000 original manipulated videos as part of its effort to accelerate the development of deepfake detection tools. It worked with 28 actors to record videos of them speaking, making common expressions, and doing mundane tasks. It then used publicly available deepfake algorithms to alter their faces...
  • Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
    The ability of an ML model to deal with noisy training data depends in great part on the loss function used in the training process. For classification tasks, the standard loss function used for training is the logistic loss. However, this particular loss function falls short when handling noisy training examples due to two unfortunate properties...
  • Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training
    Generative adversarial network (GAN) has greatly improved the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality training data while producing a small number (e.g., tens) of classes. This work aims to scale up GANs to thousands of classes meanwhile reducing the use of high-quality data in training. We propose an image generation method based on conditional transferring features, which can capture pixel-level semantic changes when transforming low-quality images into high-quality ones...
  • 5 Common Pitfalls To Avoid When Crafting Your Data Science Resume
    You're spending hours on your resume and still have no job interviews to show for it. You've gotten a lot of feedback that your resume doesn't "make sense", but you're not sure how best to describe your experiences or what specifically you should be conveying to Hiring Managers. You're not good at "tooting your own horn" but you know that some amount is needed to stand out...

 

Training*

 

 
Create D3 Data Visualizations As Fast As You Can Sketch

You need to create a D3.js data visualization to communicate your insights. But... #d3BrokeAndMadeArt! This time, your data join appears to have broken and the JavaScript console shows an error you don't recognize. Last time, you got stuck trying to figure out how to make axes that didn't look like 3rd graded made them. It makes you want to strangle D3 with your bare hands. Just how steep does the D3 learning curve need to be?!

What if you could learn and master D3 quickly and deeply?

Great news! - You can ... Check out DashingD3js.com Screencasts today!

*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!

 
 

Jobs

 
  • Senior Data Scientist - TRANZACT - NJ or Raleigh, NC

    Tranzact is a fast paced, entrepreneurial company offering a well-rounded suite of marketing solutions to help insurance companies stay ahead of the competition. The Senior Data Scientist will be solving the toughest problems at Tranzact by using data. More specifically, responsible for gathering data, conducting analysis, building predictive algorithms and communicating findings to drive profitable growth and performance across Tranzact. Must have a strong grasp on the data structure, business needs, and statistical and predictive modeling. Minimum 7 years of experience building predictive algorithms...

        Want to post a job here? Email us for details >> team@datascienceweekly.org
 

 

Training & Resources


  • Automation via Reinforcement Learning
    Let’s delve a bit more into what it means to automate a task with reinforcement learning. The basic process can be decomposed into two steps: first reduce the problem to RL by writing it as an MDP or POMDP, and then solve for the optimal policy of the MDP or POMDP2. The optimal policy then allows us to fully automate the task, completing it any number of times with no further human effort...
  • Sudo Write Me a Program: GitHub Releases the ImageNet for Code
    GitHub’s CodeSearchNet provides large datasets, tools, and benchmarks via Weights & Biases to inspire and support broader community research on source code as a language (for semantic search, understanding, translation, & more) — you can join this collaboration here...

 

Books

 

  • The Book of R: A First Course in Programming and Statistics

    "The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis"...


    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
 
Sign up to receive the Data Science Weekly Newsletter every Thursday

Easy to unsubscribe. No spam — we keep your email safe and do not share it.