Data Science Weekly Newsletter - Issue 293

Issue #293

July 4 2019

Editor Picks
 
  • State of AI Report 2019
    In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger an informed conversation about the state of AI and its implication for the future...
  • Learning to Traverse Latent Spaces for Musical Score Inpainting
    This webpage provides some sample inpainting results based on our research work titled "Learning to Traverse Latent Spaces for Musical Score Inpainting". The different models were used to generate the middle section (highlighed in green) of the melodies. Wait for a few seconds to let the file load completely before playing the examples. The examples are divided into two categories (Postively rated and Negatively rated) based on listening study that we conducted...
 
 

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

 
  • Part II: Major Trends in the 2019 Data & AI Landscape
    Part I of the 2019 Data & AI Landscape covered issues around the societal impact of data and AI, and included the landscape chart itself. In this Part II, we’re going to dive into some of the main industry trends in data and AI...
  • Building AI for Good, by the People, for the People
    Every second week we host a new challenge where a global community of AI enthusiasts collaborates to solve social problems through AI. In the following, we will share the results, after 4 weeks of work, from our first challenge...
  • A Journey from Martian Orbiters to Terrestrial Neural Networks
    How can you train a CNN on Mars? I was selected to be part of Omdena’s Global AI Collaborative Challenge, working with a 50-member team to automate the identification of landing sites and anomalies on the surface of Mars. As you have probably surmised, we decided to apply machine learning to solve this problem...
  • A new generative QA model that learns to answer the whole question
    A new question answering (QA) model that determines the correct response by reverse-engineering the question. Current state-of-the-art models are trained discriminatively, which means they stop learning when any clue lets them predict the right answer. We instead train our model to generate the question from an answer, which teaches it to explain all the clues...
  • Benefits of Overparameterization in Single-Layer Latent Variable Generative Models
    In this paper, we perform an exhaustive study of different aspects of overparameterization in unsupervised learning via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (held-out log-likelihood, recovering the parameters of the ground-truth model), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that, when learning using methods such as variational inference, larger models can significantly increase the number of ground truth latent variables recovered...
  • DL Podcast #3 | Yannic Kilcher | Population-Based Search
    In this podcast, Yannic and I talk about a lecture at ICML we both found to be really interesting. This lecture was about Population-Based Search, an extension of evolutionary algorithms. I learned a lot about this from Yannic and additionally gained a much greater understanding of adversarial examples when I asked him about his research!...
 
   
 

Jobs

 
  • Data Scientist - Visiting Nurse Service of New York - New York

    The Visiting Nurse Service of New York (VNSNY) is the nation’s largest not-for-profit home- and community-based health care organization, serving the five boroughs of New York City, and Nassau, Suffolk, and Westchester Counties. For 125 years, VNSNY has been committed to meeting the health care needs of New Yorkers with compassionate, high-quality home health care. We offer a wide range of services, programs, and health plans to meet the diverse needs of our patients, members, and clients from before birth to the end of life.

    The Data Science Team provides advanced analytical support across VNSNY’s family of corporations. We leverage big data in a fast paced environment to support strategic decisions for the agency. Meaningful, appropriate use of data is central to the success of our organization. We are looking for an ambitious data scientist to join our expanding team...

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

 

Training & Resources

 
  • Seq2Seq with Pytorch
    This is a continuation of our mini-series on NLP applications using Pytorch. In the past, we’ve seen how to do simple NER and sentiment analysis tasks, but now let’s focus our attention to another really popular architecture. In this post, we’ll illustrate some of the basics involved in creating a simple seq2seq model with Pytorch to create a dialogue agent...
 
 

Books

 

  • Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin

    "Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic"...

    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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