It's widely accepted that machine learning (ML) has been inspired largely by methods from statistical physics. As information theory, statistics and physics continue to cross-pollinate, some of the ML models are being lauded by many scientists as groundbreaking and likely to change our understanding of physical principles.
Physicists like to think that all you have to do is say: 'These are the conditions, now what happens next?'
We need more experiments embracing and applying the methods of ML. So, this website is built with practical examples in ML applied to chaos and complexity. More importance is given to computational techniques than theory. Join me as I experiment with ML and try to deduce the most important quantifiers using only a limited data set.
I have a Ph.D. in Chaos & Nonlinear Dynamics, with a passion for data and finding patterns, so I naturally gravitate towards complexity and uncertainty. I am also a tech entrepreneur with multiple successful exits and with spectacular failures too :-). Over the years, I had the good fortune of working with great engineers and scientists who have helped me with my insatiable appetite for learning, coding, and computational experiments. This website is an effort to help others and to give back to the community (in hopes of generating good karma).
For the past few years, I've been managing a team of engineers, working on interesting optimization problems, implementing ML algorithms, and poking at big data.
Hopefully, useful and practical techniques!
Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. That was before ML became a commodity in solving difficult engineering problems. In this website, I share my experimentation with ML using time-series data collected from dynamical systems that chaotic behavior. All work is done with a hacker's approach, i.e. more importance is given to delivering results (computational techniques) than to laying out the theoretical framework.
Anyone interested in learning more about chaos, complexity, and ML. That being said, there are some prerequisites: I assume you know Python and you also have basic knowledge of chaos and nonlinear dynamics. If you don't, I recommend the following books to get you started: