Edward – Tutorials

Edward provides a testbed for rapid experimentation and research with probabilistic models. Here we show how to apply this process for diverse learning tasks. Bayesian linear regression A fundamental model for supervised learning. Batch training How to train a model using only minibatches of data at a time. TensorBoard Visualize learning, explore the computational graph, and diagnose training problems. Automated transformations Using transformations to easily work over constrained continuous support. Linear mixed effects models Linear modeling of fixed and random effects. Gaussian process classification Learning a distribution over functions for supervised classification. Mixture models Unsupervised learning by clustering data points. Latent space models Analyzing connectivity patterns in neural data. Mixture density networks A neural density estimator for solving inverse problems. Generative adversarial networks Building a deep generative model of MNIST digits. Pr...

1 mentions: @dustinvtran
Date: 2019/08/12 14:16

Referring Tweets

@dustinvtran @danijarh This sounds super useful for https://t.co/XUKP7F9Exn! Currently, we duplicate text+code for both the webpage tutorial and the Jupyter notebook. Having it all as (convertable) Python scripts would be great.

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