But I'm not going to talk about how they work on an implementation, nuts and bolts level. Instead I'm going to provide a high-level framework for conceptualising all of these operations, which will hopefully provide some insight with which you'll be able to visualise these otherwise complicated, mathematical and rather opaque procedures in a more intuitive way. The core of this approach is to think of all of these operations as journeys through multiple dimensions and transformations in space. To actually implement these algorithms, it is of course essential to understand the lower level nuts and bolts, the maths, the architectures, training algorithms, hyper-parameters etc. But given a decent implementation and API, I don't think it is necessary to understand the lower level nuts and bolts just to use the algorithms.

This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy).

To call the film above surreal would be a dramatic understatement. Watching it for the first time, I almost couldn't believe what I was seeing -- actors taking something without any objective meaning, and breathing semantic life into it with their emotion, inflection, and movement. After further consideration, I realized that actors do this all the time. Take any obscure line of Shakespearean dialogue and consider that 99.5% of the audience who hears that line in 2016 would not understand its meaning if they read it in on paper. However, in a play, they do understand it based on its context and the actor's delivery.

I recently attended #alt-ai, a mini conference on Art and MI organized by Gene Kogan and folks at the School for Poetic Computation (sfpc) in New York City. The event took place in a building that was previously occupied by Bell Labs and was the location of 9 evenings almost 50 years ago. The building later became the Westbeth Artist community (home to many influential and successful artists over the years) and is now home to sfpc. The first day started with a gallery opening (about 14 pieces, many shown on Openframe.io) Gene Kogan gave an intro and a bird's eye view of the sudden explosion of interest in this field over the last year.