• The Fashion Line designed to trick Surveillance Cameras
  Adversarial Fashion garments are covered in license plates, aimed at bamboozling a device’s databases...

• New State of the Art AI Optimizer: Rectified Adam (RAdam). Improve your AI accuracy instantly versus Adam, and why it works.
  A new paper by Liu, Jian, He et al introduces RAdam, or “Rectified Adam”. It’s a new variation of the classic Adam optimizer that provides an automated, dynamic adjustment to the adaptive learning rate based on their detailed study into the effects of variance and momentum during training. RAdam holds the promise of immediately improving every AI architecture compared to vanilla Adam as a result...

• 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...

Data scientists are in demand on Vettery

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• Exploring DNA with Deep Learning
  Nice detailed post on the applications of deep learning in genetics...

• How to Build a Recommender Engine for Medical Research Papers
  A step-by-step guide to building a recommender pipeline, from data wrangling to model evaluation...

• 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...

• Unsupervised learning of landmarks by Descriptor Vector Exchange
  Can we learn unsupervised high dimensional embeddings of object structure? Our latest work DVE suggests exchanging vectors between images is key! (SoTA unsup. landmarks)...

• Joint Speech Recognition and Speaker Diarization via Sequence Transduction
  With the recent development of a novel neural network model—the recurrent neural network transducer (RNN-T)—we now have a suitable architecture to improve the performance of speaker diarization addressing some of the limitations of the previous diarization system we presented recently...

• DL on butterfly phenotypes tests evolution’s oldest mathematical model
  Deep Learning used to confirm early Theory of CoEvolution: insects evolve to mimic each other to outsmart predators...

• Building an ML−enabled fullstack application with Vue, Flask, Mongo, and Algorithmia
  Algorithmia has created a complete end-to-end tutorial to demonstrate how you can quickly build a modern ML−enabled web application using the following popular technologies...

• 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...

Create D3 Data Visualizations As Fast As You Can Sketch

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?!
What if you could learn and master D3 quickly and deeply?
Great news! - You can ... Check out DashingD3js.com Screencasts today!

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

• 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

• Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
  The best tutorials strip away all the complexity into the simplest example possible. That's what I like about this tutorial...

• 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...

• 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