Nico Filzmoser

Hi! I'm Nico 😊 I'm a technology enthusiast, passionate software engineer with a strong focus on standards, best practices and architecture… I'm also very much into Machine Learning 🤖

https://www.auroria.io

Multiple Inheritance Functionality via Strategy Pattern in Java

Decomposing seems-to-be multiple inheritance issues via the strategy pattern to achieve compositional flexibility.

Advanced Feature Attribution Techniques for Deep Learning Models

Explainable AI: Exploring various techniques built to shed light on predictions made by deep learning neural networks.

Introduction to Graph Neural Networks: The Message Passing Framework

Exploring the fundamentals of Graph Neural Networks (GNNs), their applications and the underlying mathematical principles.

Artificial Neural Network Backpropagation

Diving deep into how backpropagation of (deep) neural networks work, from derivatives and the chain rule to gradient descent and delta updatess

From Bayesian Priors to Weight Decay

Deriving via MAP estimation how Gaussian priors in the Bayesian Framework lead to the well known L₂ weight decay regularization.

Relating Bayesian Inference, Expected Risk Minimization and Maximum Likelihood Estimation

A birds-eye view on Bayesian Inference, Empirical Risk Minimization and Maximum Likelihood Estimation, relating the different concepts to find core similarities and differences.

An Overview on Matrix Factorization and Dimensionality Reduction

Exploring matrix factorization techniques such as SVD, PCA, NMF, (...) and their applications to simplify complexity in data, improve computational efficiency and reveal hidden patterns.

Reducing Docker Image Size and Enhancing Build Efficiency

Checklist-style optimization techniques for building and using docker images.

Enforcing Distributed Rate Limiting with Hazelcast and Bucket4j

Hands-on guide on enforcing distributed rate limiting for multiple instances with Bucket4J and Hazelcast.

The Transformer Architecture

Diving deep into the Transformer architecture and its mathematical underpinnings, covering scaled dot-product attention, multi-head attention, and positional encodings, ... . Exploring how encoder-decoder, encoder-only, and decoder-only models work for NLP, translation and generative AI.