Digests » 15

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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.

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.

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.

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.