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
I share daily updates on Substack, Facebook, Telegram, WhatsApp, and Viber.
Are you interested in learning how to set up automation using GitHub Actions? If so, please check out my course on LinkedIn Learning:
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.
Repo: https://github.com/OVVO-Financial/NNS
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, and a hard copy is available for purchase on Amazon.
Have any questions? Please comment below!
See you next Saturday!
Thanks,
Rami