Data Science Weekly Newsletter - Issue 300

Issue #300

Aug 22 2019

Editor Picks
 
  • A Selective Overview of Deep Learning
    From the statistical and scientific perspective, it is natural to ask: What is deep learning? What are the new characteristics of deep learning, compared with classical methods? What are the theoretical foundations of deep learning? To answer these questions, we introduce common neural network models (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets) and training techniques (e.g., stochastic gradient descent, dropout, batch normalization) from a statistical point of view...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • AI and Climate Change
    This week I talk to John Platt, a Distinguished Scientist at Google, about twin problems: finding cheap zero-carbon energy sources and mitigating global warming. John is a polymath, having discovered asteroids, helped put the touch in computer touchpads and even won an Academy Award for scientific and technical achievements in computer animation. Now, he is part of a growing movement of machine learning researchers tackling climate change...
  • SPIRAL: Pre-trained model for unconditional 19-step generation of CelebA-HQ images
    This repository contains agents and environments described in the ICML'18 paper "Synthesizing Programs for Images using Reinforced Adversarial Learning". For the time being, we are providing the libmypaint-based simulator (more coming soon) and a Sonnet module for the unconditional agent as well as pre-trained model snapshots (9 agents from a single population) available from TF-Hub...
  • How To Prepare For A Data Science Training Course
    You have decided to start a data science training program. Maybe it's a bootcamp, maybe it's a fellowship, maybe it's an apprenticeship, or maybe it's a professional degree like a masters program. In either case, you are ready to to make the most out of the situation. The only thing left to do is to prepare for the program so that you can achieve your eventual goal of getting a data science job...

 

Training*

 

 
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You need to create a D3.js data visualization to communicate your insights. But... #d3BrokeAndMadeArt! This time, your data join appears to have broken and the JavaScript console shows an error you don't recognize. Last time, you got stuck trying to figure out how to make axes that didn't look like 3rd graded made them. It makes you want to strangle D3 with your bare hands. Just how steep does the D3 learning curve need to be?!

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*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!

 

Jobs

 
  • Data Scientist - PepsiCo - NYC

    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...

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

 

Training & Resources

  • Freeing the data scientist mind from the curse of vectoRization
    In this post, we will start by solving a simple problem in R where I will try to illustrate the mindset and limitations when programming in interpreted languages. Then, we will solve the same problem with Julia, showing how the mindset differs completely and how C-like performance can be achieved out of the box...
  • Mathematics for Machine Learning
    We wrote a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books...
 
 

Books

 

  • Python Crash Course: A Hands-On, Project-Based Introduction to Programming

    Thorough introduction to programming with Python...

    "I have read multiple beginner guides to Python. I am currently up to chapter 11 in Python Crash Course. So far this is far and away my favorite Python programming book..."


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

     
    P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian
 
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