• Models for integrating data science teams within organizations
  A comparative analysis...

• The Hidden Costs of Automated Thinking
  Overreliance on artificial intelligence may put us in intellectual debt...

• Machine learning is about to revolutionize the study of ancient games
  AI, computer modeling, and data mining are tools for a new field focusing on how games have evolved...

A Data Science Playbook for Explainable ML/AI

Model ethics, interpretability, and trust will be seminal issues in data science in the coming decade. This technical webinar discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry. This new research reveals holes in traditional approaches like SHAP and LIME when applied to some deep net architectures and introduces a new approach to xML/xAI where interpretability is a hyperparameter in the model building phase rather than a post-modeling exercise. We will provide step-by-step guides that practitioners can use in their work to navigate this interesting space.
We will review code examples of interpretability techniques. You can follow along with the presentation by running your own notebook hosted in Domino's trial environment. Create a free trial account here
.
We hope to see you there! Register to attend, or to receive the webinar materials here
.

• A new tool uses AI to spot text written by AI
  This algorithm can tell if you got an AI to do your homework for you...

• Instead of practicing, this AI mastered chess by reading about it
  Machines that appreciate “brilliant” and “dumb” chess moves could learn to play the game—and do other things—more efficiently...

• New Google Technique Helps Understand How Neural Networks are Thinking
  While its relatively simple to create incredibly advanced deep neural network models, its understanding how those models create and use knowledge remains a challenge. Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors(CAVs) that takes a new angle to the interpretability of deep learning models...

• Cyclical Learning Rates with Keras and Deep Learning
  In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model...

• Building our [Stitchfix] Centralized Experimental Platform
  Running an A/B test is easy. Screwing up an A/B test is even easier. Are you sure your new feature is working as expected? Is your randomization strategy correlated with something it shouldn’t be? Do you have a proper control group for comparisons?...

• An Artist’s New Tool: How the World’s Leading Creators Use GauGAN
  GauGAN, the smart paintbrush that turns rough sketches into photorealistic masterpieces, is being used by the world’s leading creators, from art directors to concept artists at major film studios, to prototype ideas and make rapid changes to synthetic scenes...

• Is AI the next big climate-change threat? We haven’t a clue
  Dire warnings are being issued about AI’s energy needs, but new chip technologies and even AI itself could help keep demands for more electrical power in check...

• Turing Lecture: Deep Learning Revolution
  The Turing Lecture delivered by Geoff Hinton and Yann LeCun in Phoenix on June 23rd is now available. ...

• What To Do When You Have Data Experience But Lack Job Specific Technical Skills
  You have experience working with data but when you read job postings they all seem to want things you've never used or barely even heard of...

 
Hear data scientists from Airbnb, Hautelook, Target at ACTIVATE,
the Search & AI Conference

Attend the Activate Conference
and learn how to deliver hyper-personalized experiences in real-time with AI-powered search, September 9-12, 2019 in Washington DC. Hear expert-led sessions from Foot Locker, Hautelook, lululemon, StubHub, Target, Uber & Wayfair. See how industry leaders in search, AI, data science, analytics, and UX are using innovative technologies to deliver tailored digital experiences in e-commerce and the digital workplace. Register by August 9 to save $400 and save an additional 20% with code DSW20.

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

• Data Scientist, Forecasting - Spotify - NYC
  
  We seek an exceptional Data Scientist to join our Forecasting team in New York. This individual will contribute to the development of cutting-edge models to predict Spotify’s future user growth and content consumption. The output of your models will serve as the basis for the company’s financial forecast as well as provide context for business performance to both internal and external stakeholders. Your work will also help the team create a time series forecasting infrastructure that can be leveraged throughout the company. Above all, you will be at the nexus of data science and business at one of the most innovative companies in the world...

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

• Introducing tf-explain, Interpretability for TensorFlow 2.0
  A Tensorflow 2.0 library for deep learning model interpretability...

• Intro to Pyenv for Machine Learning
  If you’re doing machine learning and tired of python dependency hell, use pyenv!...

• How to Implement Pix2Pix GAN Models From Scratch With Keras
  The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. In this tutorial, you will discover how to implement the Pix2Pix GAN architecture from scratch using the Keras deep learning framework....

• 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
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  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