From Gaussian Algebra to Gaussian Processes, Part 2

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Introducing the RBF kernel, and motivating its ubiquitous use in Gaussian processes.


From Gaussian Algebra to Gaussian Processes, Part 1

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A thorough, straightforward, un-intimidating introduction to Gaussian processes in NumPy.


A Practical Guide to the "Open-Source Machine Learning Masters"

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The higher education paradigm is changing. Motivation, logistics and strategic insight re: designing the "Open-Source Masters" for yourself.


Joining ASAPP

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I'm joining ASAPP, Inc. as a Machine Learning Engineer.


Neurally Embedded Emojis

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Convolutional variational autoencoders for emoji generation and Siamese text-question-emoji-answer models. Keras, bidirectional LSTMs and snarky tweets @united within.


Random Effects Neural Networks in Edward and Keras

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Coupling nimble probabilistic models with neural architectures in Edward and Keras: "what worked and what didn't," a conceptual overview of random effects, and directions for further exploration.


Further Exploring Common Probabilistic Models

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Exploring generative vs. discriminative models, and sampling and variational methods for approximate inference through the lens of Bayes' theorem.


Minimizing the Negative Log-Likelihood, in English

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Statistical underpinnings of the machine learning models we know and love. A walk through random variables, entropy, exponential family distributions, generalized linear models, maximum likelihood estimation, cross entropy, KL-divergence, maximum a posteriori estimation and going "fully Bayesian."


Transfer Learning for Flight Delay Prediction via Variational Autoencoders

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Autoencoding airports via variational autoencoders to improve flight delay prediction. Additionally, a principled look at variational inference itself and its connections to machine learning.


Deriving the Softmax from First Principles

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Deriving the softmax from first conditional probabilistic principles, and how this framework extends naturally to define the softmax regression, conditional random fields, naive Bayes and hidden Markov models.


© Will Wolf 2017

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