New Open Source Projects, NGINX Tutorial, Running Ollama on Kubernetes, Deep Learning Book

This week's agenda:

• Open Source of the Week - new projects: supplyseer, messy, scoutbaR, targetsboard, froggeR
• Learning resources - NGINX tutorial, graph neural networks, visualize data lineage with Airflow, running Ollama on Kubernetes
• Book of the week - Deep Learning by Prof. John D.Kelleher

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Check out the weekend edition - Bluesky Data Starter Packs:

Open Source of the Week

Open-source news, updates, and new projects.

Keras

Google updated this week that Francois Chollet, the creator of Keras, is leaving the company. At this point, it is not clear how Francois's departure will impact the future of this project.

???????????????????? new release - version 0.14

The skforecast library is a Python library for time series forecasting applications with both stats and (mainly) machine learning models. Version 0.14 was released last week with new features and improvements to existing ones. More details are available on Joaquin Amat Rodrigo 's post and in the library release notes.

Documation: https://skforecast.org/0.14.0/index.html

Source code: https://github.com/skforecast/skforecast

The supplyseer Library

The supplyseer is a new Python library for applied computational supply chain & logistics applications. This library, by Jako R. and Lambert Rutaganda , provides modeling solutions for supply chain and logistics challenges, from forecasting applications to inventory optimization. More details are available on Jako Rostami's post.

Source code: https://github.com/supplyseer-ai/supplyseer/tree/develop

License: AGPL-3

The Messy Library

The messy library is a new R project by Nicola Rennie that makes a data frame messy and untidy for learning purposes. In other words, it takes academic-like datasets (e.g., nice and clean) and turns them into messy ones by adding missing values, typos, white spaces, etc. This enables the learners to encounter real-life data issues and learn how to handle them. More details are available on Nicola Rennie's post.

Documentation: https://nrennie.rbind.io/messy/

Source code: https://github.com/nrennie/messy

License: CC BY 4.0

The targetsboard Library

The targetsboard project by Athos Damiani is a new R library that provides an interactive visualization for a targets' DAG. This interactive view is based on the reactR and Shiny libraries. More details are available on Athos Damiani's post.

Source code: https://github.com/Athospd/targetsboard

License: MIT

The scoutbaR library

The scoutbaR is a new R library from the cynkra GmbH team. This library provides a scoutbar React widget for R and Shiny apps that enables the display of a model-alike navigation window. More details are available on David Granjon 's post.

Documentation: https://cynkra.github.io/scoutbaR/

Source code: https://github.com/cynkra/scoutbaR

License: MIT

The froggeR Library

The froggeR is a new R library by Kyle Grealis that provides Quarto's temples. The goal of the library is to make the work of data scientists with Quarto more efficient by leveraging built-in templates to seamlessly launch Quarto projects. More details are available in Kyle Grealis's post.

Documentation: https://kylegrealis.github.io/froggeR/

Source code: https://github.com/kyleGrealis/froggeR

License: MIT

Scrollytelling with Quarto

Last but not least is the Scrollytelling with Quarto competition by Posit PBC . The Scrollytelling is a new Quarto feature that was announced at the recent Posti Conference, which enables dynamic scoring on HTML pages. This feature was reviewed in the first edition of the newsletter. More details are available here and in the post below:

New Learning Resources

Here are some new learning resources that I came across this week.

Full NGINX Tutorial

A crash course for NGINX by Nana Janashia . This includes a demo project with Node.js and Docker settings.

Graph Neural Networks Seminar

A seminar about Graph Neural Networks at Cambridge Image Analysis group at the University of Cambridge by Maya Bechler-Speicher .

Collect and Visualize Lineage Data from your Data Pipelines with Apache Airflow

A short tutorial for setting up a data lineage process with Airflow and Marquez by George Yates .

Running Ollama on Kubernetes

The following tutorial by Mathis Van Eetvelde provides an introduction to running LLM models with Ollama and Kubernetes.

Book of the Week

If you are looking for a down-to-earth and concise resource for getting started with deep learning, the Deep Learning by Prof. John D. Kelleher book is a great choice. This pocket-size book covers the foundation of deep learning, and it includes the following topics:

• Deep learning history
• Foundations of deep learning
• Neural networks
• Convolutional and Recurrent Neural Networks (i.e., CNN and RNN)
• Learning function

The book is available for purchase on Amazon:

Meme of the Week

I could not stop laughing from Mehdi Ouazza 's post:

Have any questions? Please comment below!

See you next Tuesday!

Thanks,

Rami

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