Jonathon Shlens
I am a senior director of research and distinguished scientist at Google DeepMind interested in vision, language and learning. My organization develops artificial intelligence across multimodal domains, planning and reasoning, and fundamental science research. Some accomplishments include the invention of TensorFlow, the deployment of many production systems, and several large collaborations with Waymo. I have 25+ years of research experience, and co-authored 70+ peer-reviewed publications in top-tier conferences and scientific journals.
To learn more about my work, please see my publications and tutorials.
Tutorials
These tutorials provide a general introduction to topics I find quite interesting but often lack good explanations in textbooks or the online literature.Tutorial on Independent Component Analysis
A complete introduction and discussion of independent component analysis. Builds on previous tutorial on principal component analysis.
Version 1.0 Tutorial on Principal Component Analysis
A full introduction, description, derivation, and discussion of principal component analysis. Concrete examples for intuition building, the mathematical relation to SVD, and new extensions of this algorithm.
Version 3.02 A Light Discussion and Derivation of Entropy
A light discussion of the underlying assumptions behind entropy followed by a rigorous but simple derivation of the formula for entropy.
Version 1.01 Notes on Kullback-Leibler Divergence and Likelihood
An intuitive discussion about where Kullback-Leibler divergence arises and its relationship to likelihood theory.
Version 1.01 Notes on Generalized Linear Models of Neurons
An introduction to the application of GLMs to model neurons and networks of neurons. Brief discussion and derivation of primary equations pertaining to maximum likelihood estimation.
Version 1.51