Tensorflow.js is a way to run tensorflow model in Javascript, or simply your
browser. It is huge but not as huge as the Python tensorflow itself. The way we
use it is first, to load the 1.2MB js file from the CDN at anywhere in the HTML:
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Abe & Nakayama (2018) Deep Learning for Forecasting Stock Returns in the Cross-Section
A paper to study cross-section return, i.e., return of multiple securities at
the same point in time. The models are trivial but good to learn about its
approach to the problem.
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Solutions to LaTeX out of memory
LaTeX as a decades old system should not use too much memory. But sometimes, we
will see it run out of memory. There are various solutions to this. Here are
what I tried.
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Taylor & Letham (2018) Forecasting at Scale
This is the paper for Facebook Prophet.
It considers time series \(y(t)\) as a composition of trend, seasonality, and
holidays under generalized additive model (GAM):
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Glorot & Bengio (2010) Understanding the difficulty of training deep feedforward neural networks
This is the paper that explains what caused the gradient vanishing or exploding
problem in training neural networks. The approach was to experiment with some
fabricated image datasets as well as ImageNet datasets for multi-class
classifications. Then some theoretical derivation is provided to support the
argument.
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