Short CS Reading List for 2022

 

Short CS Reading List for 2022 

Here are a few useful recent computer science papers and books available as PDF downloads.

Machine Learning and Big Data

Some practical computation topics of current interest:

"Category Theory in Machine Learning" by Shiebler, Gavranovic, Wilson
43 pages, on arXiv uploaded 2021-06-13
arxiv.org/abs/2106.07032
Good context here for thinking about Probability Monads (e.g. Giry).   See page 22.

"Zero-Cost, Arrow-Enabled Data Interface for Apache Spark" by Rodriguez, Chackrabroty, Chu et al.
6 pages, first uploaded to arXiv on 2021-06-24, updated on 2021-11-27
arxiv.org/abs/2106.13020

"Dive into Deep Learning" interactive book
d2l.ai
1,011 page(!) compendium of ML approaches, useful for review with attempted consistent vocabulary.
Usable as HTML, intended to be agnostic about ML platform, comes in several editions. 
Stu B. uses MXNet-variant pdf download.


Popular posts from this blog

Overview of AxLam model : Nouns, Types, Morphisms

2023-Feb-03 picking up from tweet thread of 220909  AxLam model: Noun (space), Type (paint), Morphism (arrow) AxLam is for writing models using [concepts, types, and morphisms].  Our concepts may be in the form of some data, or less specifically as things we choose to name or refer to in a model.  Over this concept space, we use types and morphisms to define an adjustable descriptive model that directs and regulates processing of the concept data (or other manifestation).  Alternatively and  more approachably we may write in terms of [nouns,  formal classes, and mappings], and this framing is mostly equivalent for informal purposes. Nouns / Concepts / Terms / Data Nouns (i.e. concepts) describe anything we wish to refer to, and need not be explicit or named.  We think of nouns as the space of things we might try to describe, name or refer to.  "Terms" in a type-theoretic sense are nouns.  Real things in the human world that someone cares abo...
Read more

Streaming analytics in Scala using ZIO and Apache DataSketches (part 1)

Image
First let's look at a trivial example of streaming analytics using ZIO Streams 2.0.9 (on Scala 2.13). https://scastie.scala-lang.org/6AHfPh02S6m0k7aN6bkacA When run, this Scastie just averages some random numbers (gaussian) in a single input stream.  That code is in the trait AvgFun . But we also see a trait called ParAvgFun with the code for rather painless parallel  implementation, using ZIO chunks, via ZStream's  grouped , zipWithIndex , and groupByKey .  Across these two traits, the clean reusability of ZSinks in different pipelines shows a nice design win for the ZIO Streams API.   The code in  ParAvgFun.parAvgUsingChunks  demonstrates a terse and reasonably efficient design for accumulating results using parallel ZStreams (within a single process).  It is applicable to  simple cases where the stream elements are lightweight and similar, and the underlying operations are uniformly easy (like summing numbers).  When inste...
Read more

AxioMagic under Spark on cloud (AWS, Azure, ...)

AxioMagic under Spark on ${ cloud  } AxioMagic relies on functional composition over immutable inputs, thus matching modern evaluation frameworks like Apache Spark, which defines a structured approach to managing data contexts of functional evaluation over data frames.  Spark embodies a refinement of both  ETL and MapReduce methodology.  State machines like Spark require computation and storage resources which may come from any cluster, but today are commonly provided from cloud environments, yielding high volume functional computation at predictable usage-based costs.  We especially value Spark for open scientific dataset analysis, where it is common to work with large data files in interchange formats. Essential features of Spark distributed evaluation Spark assembles batch pipelines to pump datasets of structured records through functional tasks (which may  invoke side-effects, with all necessary stipulations).  This pipeline serves to evaluate...
Read more