Data Science Weekly Newsletter - Issue 265

Issue #265

Dec 20 2018

Editor Picks
 
  • AlphaZero: Shedding new light on the grand games of chess, shogi and Go
    Today, we are delighted to introduce the full evaluation of AlphaZero, published in the journal Science, that confirms and updates those preliminary results. It describes how AlphaZero quickly learns each game to become the strongest player in history for each, despite starting its training from random play, with no in-built domain knowledge but the basic rules of the game....
  • A Full Hardware Guide to Deep Learning
    Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high-performance system...
  • Data Science vs Engineering: Tension Points
    This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • Google’s AI Guru Wants Computers to Think More Like Brains
    WIRED caught up with Hinton last week at the first G7 conference on artificial intelligence, where delegates from the world’s leading industrialized economies discussed how to encourage the benefits of AI, while minimizing downsides such as job losses and algorithms that learn to discriminate. An edited transcript of the interview follows...
  • The Future of Software Intelligence: a Fireside Chat
    We were excited to host Jeremy Howard, Co-Founder of Fast.AI, at GitHub HQ in San Francisco on Tuesday, December 11. During the chat, Jeremy Howard discussed his thoughts on how deep learning will influence the field of Software Intelligence...
  • Using Object Detection for Complex Image Classification Scenarios
    In this series we are going to review a real world computer vision use case from the retail sector and are going to compare and contrast some of the different approaches and technologies available to solve the problem... A little over a year ago Microsoft partnered with a large manufacturer of confectionery products in Central & Eastern Europe, to build a machine learning model which validates whether distributors are stocking chocolates correctly...
  • Google AI Princeton: Current and Future Research
    Google has long partnered with academia to advance research, collaborating with universities all over the world on joint research projects which result in novel developments in Computer Science, Engineering, and related fields. Today we announce the latest of these academic partnerships in the form of a new lab, across the street from Princeton University’s historic Nassau Hall, opening early next year. By fostering closer collaborations with faculty and students at Princeton, the lab aims to broaden research in multiple facets of machine learning, focusing its initial research efforts on optimization methods for large-scale machine learning, control theory and reinforcement learning. Below we give a brief overview of the research progress thus far...
  • Dopamine
    Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research)...
 
 
 

Jobs

  • Senior Data Scientist/Machine Learning Engineer - PepsiCo eCommerce - NYC

    Want to build an RL system with real money against business experts? Apply now! PepsiCo operates in an environment undergoing immense and rapid change, driven by eCommerce and emergent retail technologies. To ensure continued success in the food and beverage space, PepsiCo has assembled a dedicated eCommerce team – tasked with optimizing eCommerce operations and developing innovations that will give PepsiCo a sustainable competitive advantage. While tied closely to broader PepsiCo, the eCommerce group more closely resembles a start-up environment; embracing the core values of having bias for action, being results oriented, maintaining a community-focus, and prioritizing people

    PepsiCo’s Data Science and Analytics group is a team of data scientists, technology specialists, and business innovators who operate within eCommerce to build industry-leading systems and solutions. By focusing on machine learning and automation, the Data Science & Analytics group is pushing the bounds of possibility for PepsiCo and its strategic partners...
 

 

Training & Resources

 
  • Comma-Separated Tree
    You’ve heard of comma-separated values (CSV)? Well, a comma-separated tree (CST) is similar, with indentation to determine the hierarchy. This gives you a hierarchical data format with the convenience and readability of CSV!...
 

 

Books

 

  • Math for Machine Learning:
    Open Doors to Data Science and Artificial Intelligence


    From self-driving cars and recommender systems to speech and face recognition, machine learning is the way of the future. Would you like to learn the mathematics behind machine learning to enter the exciting fields of data science and artificial intelligence? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step.

    This book not only explains what kind of math is involved and the confusing notation, it also introduces you directly to the foundational topics in machine learning. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.

    Praise from students
    "Your book is by far the best I’ve found for understanding the derivations of machine learning algorithms. I love that you don’t skip steps and that you provide clear examples."--Robert H"

    Link to preview of first 2 chapters and table of contents available here


    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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