Four short links: 19 February 2018

Disambiguation, Learning to Code, Open Source BI, and API Hierarchy
  1. Discovering Types for Disambiguation -- clever! Clump Wikipedia entries into categories, then use the categories to see which meaning of a word (e.g., Jaguar the car, the animal, or the aircraft) best fits the other words in the sentence.
  2. Learning to Program is Getting Harder (Allen Downey) -- The problem is that GUIs hide a lot of information programmers need to know. So, when a user decides to become a programmer, they are suddenly confronted with all the information that's been hidden from them. If someone just wants to learn to program, they shouldn't have to learn operating system concepts first. (via Slashdot)
  3. Apache Superset -- incubating a modern, enterprise-ready business intelligence web application.
  4. Exploring API Security -- an API ecosphere that is open by default, but actively identifies and minimizes harm, rather than over-complicating security requirements
    Continue reading "Four short links: 19 February 2018"

Build generative models using Apache MXNet

A step-by-step tutorial to build generative models through generative adversarial networks (GANs) to generate a new image from existing images. In our previous notebooks, we used a deep learning technique called convolution neural network (CNN) to classify text and images. A CNN is an example of a discriminative model, which creates a decision boundary to classify a given input signal (data) as either being in or out of a classification, such as email spam. Deep learning models in recent times have been used to create even more powerful and useful models called generative models. A generative model doesn't just create a decision boundary, but understands the underlying distribution of values. Using this insight, a generative model can also generate new data or classify a given input data. Here are some examples of generative models:
  1. Producing a new song or combining two genres of songs to create an entirely different
    Graph of sample human and Martian heights
    decision boundary
    generative adversarial network
    GAN training
    GAN generated images
    Continue reading "Build generative models using Apache MXNet"

Build generative models using Apache MXNet

A step-by-step tutorial to build generative models through generative adversarial networks (GANs) to generate a new image from existing images. In our previous notebooks, we used a deep learning technique called convolution neural network (CNN) to classify text and images. A CNN is an example of a discriminative model, which creates a decision boundary to classify a given input signal (data) as either being in or out of a classification, such as email spam. Deep learning models in recent times have been used to create even more powerful and useful models called generative models. A generative model doesn't just create a decision boundary, but understands the underlying distribution of values. Using this insight, a generative model can also generate new data or classify a given input data. Here are some examples of generative models:
  1. Producing a new song or combining two genres of songs to create an entirely different
    Graph of sample human and Martian heights
    decision boundary
    generative adversarial network
    GAN training
    GAN generated images
    Continue reading "Build generative models using Apache MXNet"

Four short links: 16 February 2018

Machine Design, Metrics, Layered Learning, and Automatically Mergeable Data Structure
  1. Towards Designing Machines -- survey of theory and approaches to building machines that can design things.
  2. Review of the Tyranny of Metrics (Tim Hartford) -- Rather than rely on the informed judgment of people familiar with the situation, we gather meaningless numbers at great cost. We then use them to guide our actions, predictably causing unintended damage.
  3. Physics Travel Guide -- a tool that makes learning physics easier. Each page here contains three layers which contain explanations with increasing level of sophistication. We call these layers: layman, student and researcher. These layers make sure that readers can always find an explanation they understand. One of these for security or coding would be interesting.
  4. Automerge -- A JSON-like data structure that can be modified concurrently by different users, and merged again automatically.
Continue reading Four short links: 16 February 2018.

Graphs as the front end for machine learning

The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations. In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible. Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through
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Four short links: 15 February 2018

Donut Drones, Consensus Algorithms, 2FA Spam, and Replacing Founders
  1. Donut Drone (IEEE) -- clever drone that is collision-safe. Nice!
  2. Hitchhiker's Guide to Consensus Algorithms -- In the world of crypto, consensus algorithms exist to prevent double spending. Here’s a quick rundown on some of the most popular consensus algorithms to date, from blockchains to DAGs and everything in-between.
  3. Facebook Spamming Users via Their 2FA Numbers (Mashable) -- when your profits are proportional to engagement, your business model turns your business into a junkie. It will cajole, stalk, berate, and trap users to feed its engagement addiction.
  4. What Happens When Startups Replace The Founder? (HBR) -- about 20% are replaced; noncompete laws help/hinder recruitment; it's overall beneficial; startups perform better when the founder leaves the company; raising external funding raises the probability that the founder will be replaced.
Continue reading Four short links: 15 February 2018.