Digests » 15

this week's favorite

The Future of Notebooks: Lessons from JupyterCon

Over the last two days at JupyterCon, I saw a lot of exciting ideas about the future of Jupyter notebooks. I’ve already written about my own ideas—Jupyter for debugging, Jupyter for prototyping interactions—but in this note, I want to highlight the major trends I saw in the JupyterCon presentations.

Overview of Artificial Intelligence Buzz

If you’re in tech, you’ve been hearing a lot of buzz around Artificial Intelligence, Machine Learning, and even Deep Learning. What’s the right word to be using and when? Do they all mean the same thing? I mean, people are sure using it interchangeably all the time.

Fisher Information and Natural Gradient Learning of Random Deep Networks

A deep neural network is a hierarchical nonlinear model transforming input signals to output signals. Its input-output relation is considered to be stochastic, being described for a given input by a parameterized conditional probability distribution of outputs. The space of parameters consisting of weights and biases is a Riemannian manifold, where the metric is defined by the Fisher information matrix.

Apple buys Denver startup building waveguide lenses for AR glasses

Apple has acquired Akonia Holographics, a Denver-based startup that manufactures augmented reality waveguide lenses. The acquisition was confirmed by Apple to Reuters who first reported the news.

Python Pandas: Tricks & Features You May Not Know

Pandas is a foundational library for analytics, data processing, and data science. It’s a huge project with tons of optionality and depth. This tutorial will cover some lesser-used but idiomatic Pandas capabilities that lend your code better readability, versatility, and speed, à la the Buzzfeed listicle.