Learn about data science and Julia while solving real-life problems

Read our book "Data Science in Julia for Hackers" at: https://datasciencejuliahackers.com/

Join the Not a Monad Tutorial Telegram group or channel to talk about programming, computer science and papers. See you there!

If you are looking for good engineers send me an email to mail@fcarrone.com or you can also reach me via twitter at @federicocarrone.

We put together this post to share the release of our first book on data science methods, which focuses on solving real-life problems. …

An interview with Chris Rackauckas

As we often mentioned on Not a Monad Tutorial, the world is complex, and we increasingly understand where our tools fall short when trying to model this complexity.

We’ve previously interviewed Chris Rackauckas on SciML; this time he joins us to answer questions regarding new developments in the area of symbolic computation with Julia, its relation to numerical computing, causal vs acausal approaches, how these matters are represented in Symbolics.jl and ModelingToolkit.jl, and how these packages relate to the existing simulation tooling landscape.

These packages compose easily and thus allow modelling larger, more complex systems by reusing parts, as well…

An interview with its creator, Leandro Ostera.

Source: https://abstractmachines.dev/

Here, at NAMT, we are in love with the Actor Model.
Within this paradigm, the basic units of computation are called actors. There is no shared state between them, instead, they interact via message passing. This has the advantage that actors become trivial to paralellize (in Erlang, an actor is called a process) and errors became easier to handle.

The actor model is a concurrency paradigm created by Carl Hewitt in 1973 with the goal of making the task of writing concurrent programs simpler. It is based on the idea of actors, entities…

An interview with its creator, Andy Grove

Ballista demo. Source: Andy Grove

"I have become frustrated over the years with the proliferation of Big Data tools built in JVM languages. I understand the reasons for this — Java, and especially Kotlin and Scala, are productive languages to work in, the ecosystem is very mature, and skills are widespread. However, it really isn’t the best language for these platforms. The most obvious alternative has been C++ for a long time, but I thought it would be really interesting to see what was possible with Rust." — Andy Grove

As distributed computing platforms continue to become more relevant and new programming languages emerge with…

An interview on Racket CS with programmers Gustavo Massaccesi Matthew Flatt

Still from a 2018 talk by Matthew Flatt, intervened by us

Racket flaunts the title of being the programmable programming language. With extensibility at its core, it takes metaprogramming to the next level by encouraging developers to implement their own DSLs to solve the problem at hand.

Following this same principle, its development team attacks the complexity of writing a compiler by stacking layers of DSLs to implement many of its components.

On the other hand, the project had many legacy components written in C that became a development bottleneck, so in 2017, Matthew Flatt made an announcement on a Racket Developers group:

Interview with Chris Rackauckas

We live in a complex world. For those scientists who dare to immerse themselves in that complexity and generate a deeper understanding of it, it is very common to have to deal with differential equation models that are not possible to solve without the use of a computer.

A lot of time is usually be spent in coding the particular differential equation for each problem. Julia SciML works to create and maintain tools that improve this process— from the creation of a framework that allows to automate the pipeline to create and solve problem-specific differential equations with a high level…

An interview with Stumpy creator Sean Law

Source: Stumpy documentation

In the mid-20th century, the Information Age started. Every day an astonishing amount of data is created and analyzing it in an efficient way requires computational tools that combine novel and clever approaches that benefit from cutting edge technology.

Time series are a particular kind of data: the points measured are related by time, and analyzing them can often become quite difficult because time is not just like any other variable. …

We are living in a time where more and more data is being created every day as well as new techniques and complex algorithms that try to extract the most out of it. As such, CPU capabilities are approaching a bottleneck in their computing power. GPU computing opened its way into a new paradigm for high-performance and parallel computation a long time ago, but it was not until recently that it become massively used for data science. …

A symbolic programming version of JAX

Join the Not a Monad Tutorial Telegram group or channel to talk about programming, computer science and papers. See you there!

If you are looking for good engineers send me an email to mail@fcarrone.com or you can also reach me via twitter at @federicocarrone.

SymJAX's really cool logo

As we try to have a deeper undestanding of the world we live in, we tend to add more and more complex relationships in the models we use to describe it, so we need to borrow a hand from computers to run them.

Complex relationships often are represented in form of graphs and many learning algorithms…

The third edition of BuzzConf will be held freely online via Zoom and Youtube live. We’re very proud of our speaker lineup, which covers a wide range of topics that expand the frontiers of our technical knowledge.

We will dive into the topics of functional programming, Julia, Python, data science, machine learning, observability, operating systems and more! We believe Functional Programming and Data Science are two of the most interesting topics in the field which will open many opportunities in the near future, and we want to bring the latest developments in these areas to the global community.

This conference…

Federico Carrone

A happy member of The Erlang, Rust/ML and Lisp Evangelism Strikeforce. Network Protocol’s RFC fanatic. Big Data and Machine Learning

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