The NNS Project, AI Agents with n8n, Fine Tuning LLMs with LoRa, Machine Learning Q and AI Book

This week's agenda:

• Open Source of the Week - The NNS project
• New learning resources - AI agents with n8n, fine-tuning LLMs with LoRA, lists, tuples, and sets in Python
• Book of the week - The Machine Learning Q and AI by Sebastian Raschka

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Open Source of the Week

I came across this week on the Nonlinear Nonparametric Statistics (NNS) project. This R library by Fred Viole uses partial moments or elements of variance for nonlinear analysis. This library provides a wide range of statistical applications, such as time series forecasting, regression, and classification, leveraging a nonlinear approach.

Project Highlights:

• Numerical Integration & Numerical Differentiation
• Partitional & Hierarchical Clustering
• Nonlinear Correlation & Dependence
• Causal Analysis
• Nonlinear Regression & Classification
• ANOVA
• Seasonality & Autoregressive Modeling
• Normalization
• Stochastic Dominance

Detailed examples can be found in the library documentation page:

License: GPL-3

New Learning Resources

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

Building AI Agents with n8n

The following tutorial, by Andrei Dumitrescu, focuses on building AI agents with the n8n platform (no-code).

LoRA Fine Tuning

The following tutorial by Mariya Sha provides a step-by-step tutorial for fine-tuning LLM with Lora.

List vs Tuples vs Sets

A short and concise tutorial about the differences between lists, tuples, and sets in Python, visually explained.

Book of the Week

This week's focus is on a book that breaks down ML and AI concepts - The Machine Learning Q and AI by Sebastian Raschka. This book breaks down and explains 30 core concepts of machine learning and AI. This includes the following topics:

• Neural networks and deep learning
• Computer vision
• Natural language processing
• Production and deployment
• Predictive performance and model evaluation

This book is ideal for students or folks who are at an early stage of their data science career and practitioners who want to refresh their knowledge on a specific topic.

The book is available for free, thanks to the author online:

A hard copy is available for purchase on Amazon:

Have any questions? Please comment below!

See you next Saturday!

Thanks,

Rami