Data Science Weekly Newsletter - Issue 227

Issue #227

Mar 29 2018

Editor Picks
 
  • What worries me about AI
    In this post, I’d like to raise awareness about what really worries me when it comes to AI: the highly effective, highly scalable manipulation of human behavior that AI enables, and its malicious use by corporations and governments...
  • AutoML Vision in action: from ramen to branded goods
    Take a look at the three ramen bowls below. Can you believe that a machine learning (ML) model can identify the exact shop each bowl is made at, out of 41 ramen shops, with 95% accuracy? Data scientist Kenji Doi built an AI-enabled ramen expert classifier that can discern the minute details that make one shop’s bowl of ramen different from the next one’s...
 
 

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Data Science Articles & Videos

 
  • The Beginning of the Monte Carlo Method
    This essay attempts to describe the details that led to the renascence of the statistical sampling method that became known as the Monte Carlo method as well as the roles played by various actors...
  • An Analysis of Neural Language Modeling at Multiple Scales
    Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity...
  • AI can spot signs of Alzheimer’s before your family does
    Currently, there’s no easy way to diagnose Alzheimer’s. No single test exists, and brain scans alone can’t determine whether someone has the disease. Instead, physicians have to look at a variety of factors, including a patient’s medical history and observations reported by family members or health-care workers. So machine learning could pick up on patterns that otherwise would easily be missed...
  • Learning to write programs that generate images
    In this work, we equipped artificial agents with the same tools that we use to generate images and demonstrate that they can reason about how digits, characters and portraits are constructed. Crucially, they learn to do this by themselves and without the need for human-labelled datasets. This contrasts with recent research which has so far relied on learning from human demonstrations, which can be a time-intensive process...
  • World Models: Can agents learn inside of their own dreams?
    We explore building generative neural network models of popular reinforcement learning environments[1]. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment...

 

Jobs

 
  • Data Scientist - elasticiti - NYC
    You will work on stimulating projects related to pre and post-sale media plan optimization. You will have the benefits of a broad range of enterprise-grade analytics tools as well as the space to introduce other technologies and methods. This is a new initiative where you will be setting the technical foundation and methodologies that will be employed for years to come.

    This is a full-time role working directly for one of the largest media companies in the industry here in New York City...
 
 

Training & Resources

 
  • Deep Learning Book Series · Introduction
    Series of blog posts and corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In my opinion, it is one of the bedrock of machine learning, deep learning and data science...
 
 

Books

 

  • Data Science from Scratch: First Principles with Python

    "It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science..."


    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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