∫ntegrabℓε ∂iﬀerentiαℓs · unorganised notes, code, and writings of random topics

Abe & Nakayama (2018) Deep Learning for Forecasting Stock Returns in the Cross-Section

November 30, 2021 paper

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. [more]

Solutions to LaTeX out of memory

November 28, 2021 blog system

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. [more]

Taylor & Letham (2018) Forecasting at Scale

November 8, 2021 paper

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): [more]

Glorot & Bengio (2010) Understanding the difficulty of training deep feedforward neural networks

October 15, 2021 paper

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. [more]

Thinking with type

Ellen Lupton / 2010

October 3, 2021 book

A nice book for leisure reading, and helps making better decisions on choice of fonts and page layout. It has a website too: http://thinkingwithtype.com [more]