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Data Science Weekly Newsletter
January 19, 2017

Editor's Picks

  • A short history of color theory
    Although a basic understanding of the color spectrum is rather easy to develop, color theory is an almost infinitely complex subject with roots in both science and art. It can therefore be a daunting task to learn about color composition in a way that is true to both art history and scientific truth...

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Data Science Articles & Videos

  • Scraping for Craft Beers
    If you have read some of my posts in the past, you know by now that I enjoy a good craft beer. I decided to mix business with pleasure and write a tutorial about how to scrape a craft beer dataset from a website in Python...
  • The More You Know: Using Knowledge Graphs for Image Classification
    Humans have the remarkable capability to learn a large variety of visual concepts, often with very few examples, whereas current state-of-the-art vision algorithms require hundreds or thousands of examples per category and struggle with ambiguity. One characteristic that sets humans apart is our ability to acquire knowledge about the world and reason using this knowledge. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification...
  • Scaling Recommendation Engine: 15,000 to 130M Users in 24 Months
    Delivering users with precise product recommendations (recs) is the creative force that drives Retention Science to continue to iterate, improve and innovate. In this post, our team unveils our iteration from a minimum viable product to a production-ready solution...


  • Data Scientist & Machine Learning Researcher - American Express - NYC
    As a Data Scientist in the Machine Learning and Data Science Team, you will help American Express accelerate its digital transformation. You will be challenged with designing winning data products and developing new big data capabilities that will elevate American Express to the forefront of the digital revolution...

Training & Resources

  • The Anatomy of Deep Learning Frameworks
    In this post, I have tried to sketch out these common principles which would help you better understand the frameworks and for the brave hearts among you, provide a guide on how to implement your own deep learning framework...
  • Machine Learning for Artists
    In general, this book will try to minimize the use of math, and rely on visual aides more than equations, both because neural networks can be well understood this way, and because it helps reduce the need for other qualifications...


  • Algorithms for Data Science
    "This groundbreaking textbook on practical data analytics unites fundamental principles, algorithms, and data. Programming fluency and experience with real and challenging data sets are gained through more than 20 Python and R tutorials and lots of exercises with solutions."...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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