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Machine Learning Articles

Machine learning, deep learning, artificial intelligence... The science of getting machines to perform actions without explicitly programming them to do so can be intimidating for the uninitiated. These machine learning articles aim to unpack the black box for beginners, with introductions to overall concepts and tutorials for training a model of their own.

Descriptive mAchine Learning EXplanations (DALEX)

Unpack some black boxes with this handy cheatsheet for understanding how complex ML models work. - Przemyslaw Biecek

The Malicious Use of Artificial Intelligence

This 101-page report “surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats.” Divvy it out across your commutes and moments of downtime this week. -

Manifesto for Data Practices

Give this a read, whether you sign it or not. -

So, How Many ML Models You Have NOT Built?

“What will put us out of our job is Machine Learning Overkill. I have seen implementation of Machine Learning algorithms to very frivolous problems and worse still the companies have invested heavily into the idea. It is a ticking time bomb. The moment the companies realize that the ROI is negative, they will shun the Data Science practice altogether.” - Towards Data Science

THREAD: How computer vision and natural-language processing systems reflect societal stereotypes

A rabbit hole worthy of your time: various types of machine learning bias as tracked by academic papers. - Arvind Narayanan

Exploring Recommendation Systems

In practice, recommenders don’t always work as well as we’d like them to. This post sets out to discover why. - FastForward Labs

Turning Design Mockups Into Code With Deep Learning

Ever wish you could automate the front-end engineering process? Here’s how to teach a neural network to code a basic HTML and CSS website from a design mockup. - FloydHub

Learning Curves for Machine Learning

How do you diagnose bias and variance? And what actions should you take once you’ve detected these errors? - Dataquest

2017: The year AI beat us at all our own games

“Over the past 12 months AI crossed a series of new thresholds, finally beating human players in a variety of different games, from the ancient game of Go to the dynamic and interactive card game, Texas Hold-Em Poker.” - New Atlas

Machine Learning: The High-Interest Credit Card of Technical Debt

There’s no such thing as a free machine learning project. Avoid or refactor these risk factors and design patterns to keep technical debt from piling up. - Research at Google

How many images do you need to train a neural network?

The technically correct answer is: “It depends.” The ballpark answer is: “1,000 representative images for each class.” (With some caveats of course.) - Pete Warden

Deep Learning Achievements Over the Past Year

Carve out some time in your holiday schedule to explore 2017's most exciting developments in text, voice, and computer vision technologies. - Stats & Bots

Deep Learning Achievements Over the Past Year

Carve out some time in your holiday schedule to explore 2017's most exciting developments in text, voice, and computer vision technologies. - Stats & Bots

The U.S. Leads in Artificial Intelligence, but for How Long?

Government policies such as the tax bill, reduced funding, and tightening of rules on immigration for international researchers threaten the U.S.’s advantage in AI. - MIT Technology Review

NIPS 2017 — Highlights

If you didn’t attend the conference on Neural Information Processing Systems last week, never fear! Catch up on the latest in AI with these day-by-day summaries. - Insight Data

Improving Palliative Care with Deep Learning

80% of Americans prefer to spend their final days in their home, but only 20% actually do. This 18-layer deep neural network identifies hospitalized patients with a high risk of death in the next 3-12 months, so they can get access to palliative care sooner. - Standford ML Group

[VIDEO] Livecoding Madness: Let’s Build a Deep Learning Library

This is interesting on two levels: “how to build a deep learning library” and “how someone who’s not me writes Python” (in this case, the answer is: incredibly fast). - Joel Grus

Innovating Faster on Personalization Algorithms at Netflix Using Interleaving

“The interleaving approach allows us to quickly prune down the initial set of ranking algorithms to the most promising candidates, enabling us to conduct experiments a rate much faster than traditional A/B testing to identify winning ideas.” - Netflix Technology Blog

Fairness Measures

Awareness of the bias of algorithms is important, but here’s a way to actually do something about it. Run your dataset through this Python package and you’ll get back a measure that quantifies discrimination within that dataset. - Fairness Measures

The era of easily faked, AI-generated photos is quickly emerging

Nvidia’s researchers trained algorithms on 30,000 images of celebrities, and it’s nearly impossible to tell the generated images from the real ones. - Quartz

Scalable Machine Learning (Part 1)

What do you do when your training dataset fits in memory, but the dataset you're making predictions on doesn't? This post identifies where the usual pandas and scikit-learn for in-memory analytics workflow breaks down and offers some solutions for scaling out to larger problems. - Tom Augspurger

Can Neural Nets Detect Sexual Orientation? A Data Scientist’s Perspective

Dig into the data behind Stanford's controversial paper Deep Neural Networks Can Detect Sexual Orientation From Faces. -

My Neural Network isn't working! What should I do?

11 mistakes you may make while implementing a neural network—and how to fix them. - Daniel Holden

Train, Score, Repeat, Watch Out! Zillow's Andrew Martin on modeling pitfalls in a dynamic world.

One of Zillow's data scientists addresses the challenges that don’t crop up in standard textbook problems or most ML competitions: feedback loops, dynamic datasets, and temporal consistency. A great read for Kagglers and non-Kagglers alike. - No Free Hunch

Switching to a Probabilistic Model for Venue Search in Foursquare

How Foursquare’s engineering team improved the accuracy and user experience of their location intelligence by switching from a search ranking algorithm to regression trees and probabilities. - Foursquare Engineering

BuzzFeed News Trained A Computer To Search For Hidden Spy Planes. This Is What We Found.

Learn how BuzzFeed trained a random forest algorithm to spot planes flown by the FBI and DHS. - BuzzFeed

Technical Debt in Machine Learning

What do feedback loops, correction cascades, and hobo-features have in common? They’re all machine learning anti-patterns that can slowly creep into your infrastructure and create a ticking time bomb. - Towards Data Science

Inside Facebook’s AI Workshop

When Joaquin Candela first started at Facebook, he worked on an ad-targeting algorithm with a handful of engineers. Five years later, he runs the Applied Machine Learning team, which comprises hundreds of employees running thousands of experiments a day. Here’s how he scaled up Facebook’s AI factory at breakneck speed. - Harvard Business Review

Using Machine Learning to Predict Value of Homes On Airbnb

How Airbnb used internal and open-source tools (like Python!) to lower the overall development costs of customer lifetime value (LTV) modeling. Code examples abound. - Airbnb Engineering and Data Science

Improving the Realism of Synthetic Images

Producing a large, diverse, and accurate training set for machine learning models is a pricey endeavor. Apple provides a rare behind-the-scenes look at how they cut costs and improved their models by making simulated images look more realistic. - Apple Machine Learning Journal

Human-Centered Machine Learning

For UX folks: A 7-step guide to stay focused on human needs when designing with machine learning. - Google Design

Visualizing High Dimensional Data In Augmented Reality

When you’re trying to understand the relationships in a really big dataset (three-million-grocery-orders big), a 2D scatterplot might not cut it. This immersive 3D visualization technique offers a way to make sense of data with multiple attributes and improve machine learning features and models. - Inside Machine Learning

How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native

The use-case may be farcical, but the deep learning and edge computing behind it are very real. - Hacker Noon

Predicting the Success of a Reddit Submission with Deep Learning and Keras

It all comes down to two things: the time of day and a catchy title. - Max Woolf

Vertical AI Startups: Solving Industry-specific Problems by Combining AI and Subject Matter Expertise

“While most of the machine learning talent works in big tech companies, massive and timely problems are lurking in every major industry outside tech.” - Bradford Cross

J.P. Morgan’s massive guide to machine learning and big data jobs in finance

Get the key takeaways from this 280-page report, including essential data analysis packages, hiring tips, and which machine learning techniques to apply to which problems. - efinancialcareers

“Many enterprise ‘AI products’ and ‘machine intelligence’ products built today have limited appeal or impact”

One investor’s self-described “unpopular” opinion - Sarah Guo

Is Your Organization Ready for ML?

Don’t make this mistake: “[M]any organizations rush to hire ML experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization.” - RE•WORK


Save this hashtag for the moments when you need to jog your memory on some basic concepts. - Chris Albon

Machine Learning for Product Managers

A brilliant, non-technical read for anyone who designs, supports, manages, or plans for products that use machine learning. - Hacker Noon

Distill: An Interactive, Visual Journal for Machine Learning Research

This new online publication is bringing academic journals into the 21st century: “A Distill article… isn’t just a paper. It’s an interactive medium that lets users – 'readers' is no longer sufficient – work directly with machine learning models.” - Y Combinator

Tips & Tricks for Feature Engineering / Applied Machine Learning

One commenter put it best: 'Probably the best feature engineering slides I have found [on] the internet.' Need we say more? - HJ van Veen

Learning about Machine Learning with an Earthquake Example

How well can we predict whether or not someone is prepared for an earthquake? - Simply Statistics

How Fitbit’s data science team scales machine learning

Workout regimens need to be tailored to each individual. Directional correctness isn’t enough. Fitbit’s head of data science shares how his team builds a model for every user to increase motivation and prevent injuries. - Mixpanel

Fake News Challenge

This grassroots effort is inviting teams to harness AI technologies to help human fact checkers identify hoaxes and deliberate misinformation in news stories. The top three teams get a cash prize, so grab a couple of friends and check out the training dataset. - Fake News Challenge

Machine Learning Videos

More of a visual learner? Here’s a repository of recorded talks at machine learning conferences, workshops, seminars, and more. - Dustin Tran

What is artificial intelligence? A three part definition

“As soon as it works, no one calls it AI anymore.” - Simply Statistics


You could be a poet, and not know it. Feed the works of your favorite author through this new Python library to generate as many lines of verse as you want. - Anthony Federico

What I Learned Implementing a Classifier from Scratch in Python

With libraries like scikit-learn, it’s easy to run an algorithm on some data and automagically get an answer—without understanding exactly how you arrived there. Prepare to unpack the black box. - Jean-Nicholas Hould

What’s the state of the job market in data science and machine learning?

“Th[e] proliferation of courses, resources, books and startups would hint that machine learning is becoming more and more accessible to the average programmer and that the market is on track to getting saturated quickly. Is this the current trend?” - Hacker News

20 Weird & Wonderful Datasets for Machine Learning

Getting your hands on a robust dataset is the hardest part of machine learning. Finding interesting datasets is tougher still. From UFO sightings to beautiful Flickr photos, you’re sure to find something to train your model. - Oliver Cameron

Deep-Fried Data

Opening your data can lead to unpredictable benefits, but requires being open to unexpected uses of your data. - Idle Words

Deep Learning Isn’t a Dangerous Magic Genie. It’s Just Math

This essay is a godsend for those of us who have trouble understanding or explaining what exactly deep learning is. - WIRED

Boosting Sales With Machine Learning

One developer shares how his team used natural language processing and machine learning in Python to pre-qualify sales leads so reps don’t have to spend hours doing it manually. - Xeneta

Hybrid Intelligence: How Artificial Assistants Work

When humans and machines work together, they accomplish a lot more than either could on their own. This is known as hybrid intelligence—a pretty intimidating term for those unfamiliar with machine learning. Here’s a breakdown. - Clare Corthell

The real prerequisite for machine learning isn’t math, it’s data analysis

Machine learning amateurs, take heart. Proficiency with high level math may be essential for machine learning theory. But with out-of-the-box tools like R’s gmodels package or Python’s scikit-learn library, you don’t need to know linear algebra or calculus to build a successful predictive model. You do, however, need to know your way around a dataset. - Sharp Sight Labs

How Kalman Filters Work, Part 1

This article unpacks different filtering algorithms in an incredibly intuitive way. It’s a long read, but you’ll come away having learned a ton (did you know that NASA used Kalman filters to help Apollo spacecraft navigate to the moon?). - An Uncommon Lab

Explained Visually

This website is an incredible collection of interactive visualizations aimed at making tricky concepts like Markov chains and regression easy to understand. Schedule a few hours to explore this one—you’re gonna need them. - Explained Visually

Microsoft’s Tay is an Example of Bad Design

0r Why Interaction Design Matters, and so does QA-ing. - Caroline Sinders

Why Microsoft Accidentally Unleashed a Neo-Nazi Sexbot

It’s not surprising that Microsoft’s chatbot spewed racist invective, but here’s how it could have been avoided. - MIT Technology Review

Here's How We Prevent The Next Racist Chatbot is the consequence of poor training - Popular Science

Lift analysis - A data scientist’s secret weapon

Learn how to spot flaws in machine learning models with lift analysis (and why you should add it to your list of evaluation metrics). - Andy Goldschmidt

We Now Have Algorithms To Predict Police Misconduct

You’ve probably heard of predictive policing, but what about predictive policing for the police? One police department teamed up with researchers to test an algorithm that detects troublesome behavior of officers early on. - FiveThirtyEight

Are Your Predictive Models like Broken Clocks?

How can you ensure you’ve picked the “right model” for a very big and very complex dataset? - Rocket-Powered Data Science

Startups Aim to Exploit a Deep-Learning Skills Gap

What do you do when every company wants to build a deep-learning network, but the experts are in short supply? Launch a product, of course. Some startups have created computer chips and software libraries that can accelerate algorithm training, all without having to hire an experienced team of deep-learning experts. - MIT Technology Review

Georgia Tech Researchers Demonstrate How the Brain Can Handle So Much Data

Random projection is frequently used in machine learning to make sense of big, diverse data. It turns out this method could be one of the ways that humans learn, too. - Georgia Tech

The current state of machine intelligence 2.0

These days, it feels like every other article in our newsfeeds is touting the potential of machine intelligence. This article cuts through the hype and presents this year’s major accomplishments in two categories—“(1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.” - O'Reilly