Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users   Modern deep learning methods constitute incredibly powerful tools to tackle a
myriad of challenging problems. However, since deep learning methods operate as
black boxes, the uncertainty associated with their predictions is often
challenging to quantify. Bayesian statistics offer a formalism to understand
and quantify the uncertainty associated with deep neural network predictions.
This tutorial provides an overview of the relevant literature and a complete
toolset to design, implement, train, use and evaluate Bayesian Neural Networks,
i.e. Stochastic Artificial Neural Networks trained using Bayesian methods.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
13
The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn