Data Science Weekly Newsletter - Issue 257

Issue #257

Oct 25 2018

Editor Picks
 
  • A neural network designs Halloween costumes
    It’s hard to come up with ideas for Halloween costumes, especially when it seems like all the good ones are taken. And don’t you hate showing up at a party only to discover that there’s *another* pajama cardinalfish? So, I wanted to find out if a neural network could help invent Halloween costumes...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • Highlights from RecSys 2018
    Here is a summary of the recent Conference on Recommender Systems I wrote with my Spotify colleagues Zahra Nazari and Ching-Wei Chen...
  • Trellis Networks for Sequence Modeling
    We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. ...
  • deeplabv3
    PyTorch implementation of DeepLabV3, trained on the Cityscapes dataset...
  • Partial Convolutions for Image Inpainting using Keras
    Keras implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions"... it's been a great learning experience for me to implement the architecture, the partial convolutional layer, and the loss functions...
  • Curiosity and Procrastination in Reinforcement Learning
    Reinforcement learning (RL) is one of the most actively pursued research techniques of machine learning, in which an artificial agent receives a positive reward when it does something right, and negative reward otherwise. This carrot-and-stick approach is simple and universal, and allowed DeepMind to teach the DQN algorithm to play vintage Atari games and AlphaGoZero to play the ancient game of Go. This is also how OpenAI taught its OpenAI-Five algorithm to play the modern video game Dota, and how Google taught robotic arms to grasp new objects. However, despite the successes of RL, there are many challenges to making it an effective technique...
 
 
 

Jobs

  • Data Scientist - Hearts & Science - NYC

    As every CMO knows, employing technology, data and analytics to business challenges is the cost of entry, not a nice-to-have. Unlocking the value of consumer data can deliver growth, revenue and ROI, and create differentiation to stay on top. Real-time action is instrumental in driving results within a highly fragmented marketplace. Hearts & Science’s Marketing Science solutions are built to deliver insightful results with speed, accuracy and a single version of the truth. In leveraging the DNA of a marketing agency with a talent pool of developers, data scientists and Ph.Ds, we hold a unique position in the increasingly crowded ad tech and consulting space...
 

 

Training & Resources

 
  • Trellis Networks for Sequence Modeling
    We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. ...
 

 

Books

 

  • Data Visualization with Python and JavaScript:
    Scrape, Clean, Explore & Transform Your Data

    Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations...


    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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