Data Science Weekly - Issue 207
Issue #207 Nov 9 2017
Editor Picks
Feature Visualization
Have you ever wondered what goes on inside neural networks? Feature visualization is a powerful tool for digging into neural networks and seeing how they work. Our new article, published in Distill, does a deep exploration of feature visualization, introducing a few new tricks along the way!...
Backing off towards simplicity - why baselines need more love
Controversial claim: In deep learning, most models are overpowered for what they need to achieve. This leads to slower and more complex models, misleading human intuition and poisoning forward progress, especially when compared against sub-optimal baselines. When we lose accurate baselines, we lose our ability to accurately measure our progress over time...
More Evidence That Humans and Machines Are Better When They Team Up
Instead of just fretting about how robots and AI will eliminate jobs, we should explore new ways for humans and machines to collaborate, says Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL)...
A Message from this week's Sponsor:
Transform data into something meaningful.
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Data Science Articles & Videos
Despite All Our Fancy AI, Solving Intelligence Remains “the Greatest Problem in Science”
Autonomous cars and Go-playing computers are impressive, but we’re no closer to machines that can think like people, says neuroscientist Tomaso Poggio...
Using neural networks to detect car crashes in dashcam footage
In this post, I will describe how, as a Fellow for Insight Data Science, I built a classification machine learning algorithm (Crash Catcher!) that employs a hierarchical recurrent neural network to isolate specific, relevant content from millions of hours of video...
Scraping Reddit to find the most popular domains
Reddit is one of the 10 most popular sites on the internet, making it a great way to find independent content creators (either for advertising purposes, or simply content discovery)...
Numerical Computation for Deep Learning - AI With The Best
Nice overview of numerical issues encountered in ML...
mixup: Data-Dependent Data Augmentation
By popular demand, here is my post on mixup, a new data augmentation scheme that was shown to improve generalization and stabilize GAN performance...
Learning in Cycles Implementing Sustainable Machine Learning Models in Production
Done poorly, repeated models can amplify the errors and biases of their initial versions. But when done right, they can learn from those mistakes over time, and employ the results of previous versions as new training data to keep the model fresh and productive over the course of months or years of applied use. With examples from my own work in the political, nonprofit, and civic data science fields, this talk will introduce a framework for designing machine learning models that get better over time...
Fully-Parallel Text Generation for Neural Machine Translation
Today Salesforce is announcing a neural machine translation system that can overcome this limitation, producing translations an entire sentence at a time in a fully parallel way. This means up to 10x lower user wait time, with similar translation quality to the best available word-by-word models...
Deep Learning Architecture Genealogy Project
See important deep learning algorithms (and papers) from one mindmap...
Jobs
Machine Learning / AI Architect – Research & Development -
Citrix - Patras, Greece Citrix is expanding its Advanced Analytics team, with seasoned professionals in the ML/AI/Data Science and Security domains. You will join a crack team, with years of history delivering high-quality Analytics products, and a global outreach. You will be collaborating with fellow engineering teams across the globe, on the cutting edge Citrix Analytics Service
Key Responsibilities:Research & develop Machine Learning models for security problems, in the areas of Networking, Application & Data.
Suggest, collect and synthesize requirements and create effective features.
Apply research methodologies to identify the Machine Learning models for the problem at hand
Training & Resources
An Overview of ResNet and its Variants
Since ResNet blew people’s mind in 2015, many in the research community have dived into the secrets of its success, many refinements have been made in the architecture. This article is divided into two parts, in the first part I am going to give a little bit of background knowledge for those who are unfamiliar with ResNet, in the second I will review some of the papers I read recently regarding different variants and interpretations of the ResNet architecture...
Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language
Pyro is a tool for deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. The goal of Pyro is to accelerate research and applications of these techniques, and to make them more accessible to the broader AI community...
Copista: Training models for TensorFlow Mobile
In this part, you can find the tools and tricks used to train mobile models for Neural Style Transfer Android application based on TensorFlow Mobile: Copista — Cubism, expressionism AI photo filters...
Books
Our Final Invention: Artificial Intelligence and the End of the Human Era "Well written exploration of the precipice that we are approaching..."
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. We just have a few slots left in 2017 - grab a spot now; first come first served! Email us for more details - All the best, Hannah & Sebastian