A Glossary of Deep Learning
This is a notebook, a place to record what I learn as I explore a new frontier of applied artificial intelligence. My aim is to explain the key concepts in simple language, because complexity doesn’t have to mean complicated.
Essays
AI Adventures in The Library of Babel
- On Generative AI and the Automated Search for Meaning
You Can Not Split The Atom In Your Head
- On AI Risk, Technology Anxiety, and starting before we feel ready
Foundation Concepts
- Activation Function —why we make outputs non-linear
- Autoencoder — how to learn the essence of your input
- Backpropagation — how networks learn from mistakes
- Batch Normalisation — inter-layer transforms improve stability
- Bias — shifts the activation function to better fit the data
- Dynamic Memory Networks (DMNs)
- Embedding — how to turn words into numbers
- Error —how measures of inaccuracy enable learning
- …
About The Glossary
The image of The Pleiades above provides what I consider a fitting visual metaphor for the nodes of a neural network. Some activating brilliantly, others dimly. All widely distributed, yet still interacting subtly. Considered individually, each is just a blazing star, but collectively, at the right distance, beauty emerges…
The format of this glossary is inspired by Marvin Minsky’s AI classic The Society of Mind, a collection of short posts, where I can record what I’ve encountered, and perhaps teach what I’ve found.
Being a glossary, there’s no need to read posts in chronological order, just read whatever topics catch your interest. As there’s no narrative, this post will serve as the index, and will be updated as new posts are added. If you find it useful, you might like to bookmark it.
Medium is a collaborative environment, so if you have something you’d like to add to anything that I’ve written, just highlight the text and add it as a margin note, or click on the speech bubble to enter a comment. And if you do find an entry particularly useful, do click on the green heart, it will help others find it.
I hope you find it useful.
