This post is going to be about what I think good documentation is and how I think you should go about writing it. I’m not perfect, so you should take everything with a grain of salt, but I hope you’ll find it useful and thought-provoking even if you don’t agree with me about everything.
Richard P Gabriel: "Engineering often precedes science. Incommensurability is real."
Scott Berkun: "As part of my recent talk about getting the most from events and championing ideas, I mentioned a brief theory on how to take notes. I’m presenting an updated version of the talk as the closing session of An Event Apart Boston tomorrow and wanted to share some advice early on."
Video projectors are one of the most important tools for creators of interactive installations. The information for projectors is available on various websites, but this 2 part guide will focus on their use in production and interactive environments. Part 1 will focus on the projector as hardware(lenses, lumens, surfaces etc), and part 2 will go into more details on the software and calibration considerations. This will gather some of that disparate knowledge into the important bits you need to make informed decisions about creating a high quality experience.
Book (full text) from 1993 explaining how to use C as a fully object-oriented language. Curious to see how many of the techniques in this book everyone already discovered for themselves.
Marvin Minsky’s book "The Society of Mind" in HTML, Creative Commons licensed. The nicest person I was ever terrified to meet.
Neural Networks and Deep Learning is a free online book. The book will teach you about:
Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you all the core concepts behind neural networks and deep learning.
(Note — there’s an indiegogo campaign to support the writing of this book)
Deep learning has become something of a buzzword in recent years with the explosion of ‘big data’, ‘data science’, and their derivatives mentioned in the media. Justifiably, deep learning approaches have recently blown other state-of-the-art machine learning methods out of the water for standardized problems such as the MNIST handwritten digits dataset. My goal is to give you a layman understanding of what deep learning actually is so you can follow some of my thesis research this year as well as mentally filter out news articles that sensationalize these buzzwords.
In such thrillers, the mystery within the mystery is: Does anything mean anything? Can anything mean?