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Data Science Weekly Newsletter
November 9, 2017

Editor's Picks

  • Feature Visualization
    Have you ever wondered what goes on inside neural networks? Feature visualization is a powerful tool for digging into neural networks and seeing how they work. Our new article, published in Distill, does a deep exploration of feature visualization, introducing a few new tricks along the way!...
  • Backing off towards simplicity - why baselines need more love
    Controversial claim: In deep learning, most models are overpowered for what they need to achieve. This leads to slower and more complex models, misleading human intuition and poisoning forward progress, especially when compared against sub-optimal baselines. When we lose accurate baselines, we lose our ability to accurately measure our progress over time...

A Message From This Week's Sponsor

Transform data into something meaningful.

A master’s in business analytics from Clark University’s Graduate School of Management will equip you with the skills needed for the high-demand field of data analysis. You’ll learn how to formulate insights and communicate your knowledge effectively to data scientists, executives and peers. Our hybrid online and on-campus model allows you to earn your degree in only one year. With a strong commitment to the Principles for Responsible Management Education (PRME), ethics and corporate responsibility are integrated into our program, providing graduates a skill set that uniquely positions them among their peers.

Data Science Articles & Videos

  • Using neural networks to detect car crashes in dashcam footage
    In this post, I will describe how, as a Fellow for Insight Data Science, I built a classification machine learning algorithm (Crash Catcher!) that employs a hierarchical recurrent neural network to isolate specific, relevant content from millions of hours of video...
  • Learning in Cycles Implementing Sustainable Machine Learning Models in Production
    Done poorly, repeated models can amplify the errors and biases of their initial versions. But when done right, they can learn from those mistakes over time, and employ the results of previous versions as new training data to keep the model fresh and productive over the course of months or years of applied use. With examples from my own work in the political, nonprofit, and civic data science fields, this talk will introduce a framework for designing machine learning models that get better over time...
  • Fully-Parallel Text Generation for Neural Machine Translation
    Today Salesforce is announcing a neural machine translation system that can overcome this limitation, producing translations an entire sentence at a time in a fully parallel way. This means up to 10x lower user wait time, with similar translation quality to the best available word-by-word models...


  • Machine Learning / AI Architect – Research & Development -
    Citrix - Patras, Greece

    Citrix is expanding its Advanced Analytics team, with seasoned professionals in the ML/AI/Data Science and Security domains. You will join a crack team, with years of history delivering high-quality Analytics products, and a global outreach. You will be collaborating with fellow engineering teams across the globe, on the cutting edge Citrix Analytics Service

    Key Responsibilities:
    • Research & develop Machine Learning models for security problems, in the areas of Networking, Application & Data.
    • Suggest, collect and synthesize requirements and create effective features.
    • Apply research methodologies to identify the Machine Learning models for the problem at hand

Training & Resources

  • An Overview of ResNet and its Variants
    Since ResNet blew people’s mind in 2015, many in the research community have dived into the secrets of its success, many refinements have been made in the architecture. This article is divided into two parts, in the first part I am going to give a little bit of background knowledge for those who are unfamiliar with ResNet, in the second I will review some of the papers I read recently regarding different variants and interpretations of the ResNet architecture...
  • Copista: Training models for TensorFlow Mobile
    In this part, you can find the tools and tricks used to train mobile models for Neural Style Transfer Android application based on TensorFlow Mobile: Copista — Cubism, expressionism AI photo filters...


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