Thoughts on machine learning, complex systems, and the intersection of technology with society.
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Parte 1 de uma série em cinco artigos sobre o currículo atual da Professional Machine Learning Engineer da Google Cloud.
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Parte 2 da série PMLE: qualidade de dados, BigQuery ML, Feature Store, skew, drift e pipelines de preparação.
Parte 3 da série PMLE: notebooks, treino, tuning, avaliação, experimentação e Model Registry.
Parte 4 da série PMLE: inferência batch e online, endpoints, pipelines, retraining e monitorização.
Parte 5 da série PMLE: Model Garden, Agent Builder, avaliação generativa, fairness, privacy e safety.
Os post-mortems leem-se todos da mesma maneira. Um guia de sobrevivencia para gestores de projecto e os que tem de lidar com eles.
Everything nobody told you before putting an LLM in production. A deep dive into the hidden trade-offs of AI development.
The ten deadly sins of project management and their respective penances. A survival guide for dealing with clients and expectations.
From 17th-century calculus to the deep learning revolution: tracing the key milestones in the development of artificial neural networks.
Fine-tuning encoder transformers for music emotion recognition using Russell's Circumplex Model.
How large language models work: token prediction, pseudo-memory, and the invisible labour behind AI systems.
The algorithm is not a tool; it is an environment. We do not use it; we inhabit it.
Three interactive simulations. Simple rules. Complex patterns.
Interactive visualization of complex patterns emerging from simple local rules.
Why most AI projects fail before production: from garbage data to organizational blindspots.
What data leakage is, why it silently inflates metrics, and how to prevent it.
Why feature selection matters, what to remove, and how to improve your model.
A curated list of resources to enhance your scientific writing and research workflow.
Essential tips for crafting a compelling presentation for your thesis defense.