learning
Links to this note
- open documents
- Data analysis using regression and multilevel hierarchical models by Andrew Gelman and Jennifer Hill in 2006
- Experiential learning by David Kolb in 2015
- An approximation to an efficient fundamental physics curriculum
- Probabilistic Modeling and Statistical Inference by Michael Betancourt in 2019
- Biology of information lectures at College de France
- Bayesian informal logic and fallacy by Kevin Korb in 2003
- The 2010s Our Decade of Deep Learning Outlook on the 2020s by Jürgen Schmidhuber in 2020
- free energy principle
- The free energy principle for action and perception A mathematical review by Christopher Buckley, Anil Seth, et al in 2017
- Russ Salakhutdinov's lectures on Large Scale Machine Learning
- probabilistic inference
- variational inference
- Active inference on discrete state-spaces - a synthesis by Lancelot Da Costa, Thomas Parr, Karl Friston et al in 2020
- variational autoencoder
- reinforcement learning
- phase transition
- metacademy
- manim
- information geometry
- hacker news
- applications of stochastic processes
- Whatever next? Predictive brains, situated agents, and the future of cognitive science by Andy Clark in 2013
- The 2010s Our Decade of Deep Learning Outlook on the 2020s by Jürgen Schmidhuber in 2020
- Russ Salakhutdinov's lectures on Large Scale Machine Learning
- Pragmatism, Objectivity, and Experience by Steven Levine in 2019
- Pattern recognition and machine learning by Chris Bishop in 2006
- Optimal transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming by Geoffrey Schiebinger, Aviv Regev, Eric Lander, et al in 2019
- Markov Processes for Stochastic Modeling by Oliver Ibe in 2013
- Intelligence and spirit by Reza Negarestani in 2018
- How to construct and debug a dockerfile
- Free Energy, Value, and Attractors by Friston and Ao in 2012
- Expanding the Active Inference Landscape More Intrinsic Motivations in the Perception-Action Loop by Martin Biehl, Daniel Polani, et al in 2018
- Du Phan
- Becoming as inference
- An introduction to probabilistic programming by Jan-Willem van de Meent, Frank Wood et al in 2018
- A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
- A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta, Charles Fisher, David Schwab, et al in 2018