<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Devis Nishku</title><description>Articoli e progetti di Devis Nishku.</description><link>https://devisnishku.com/</link><language>it-IT</language><atom:link href="https://devisnishku.com/rss.xml" rel="self" type="application/rss+xml"/><item><title>Localizzazione multi-persona con radar UWB su microcontrollore</title><link>https://devisnishku.com/progetti/uwb-radar-multi-person-localization/</link><guid isPermaLink="true">https://devisnishku.com/progetti/uwb-radar-multi-person-localization/</guid><description>Sistema TinyML che localizza fino a 4 persone da 6 radar UWB: CNN int8 da 90,3 KB per ESP32-S3, F1 di 0,907 sulle finestre di validazione.</description><pubDate>Thu, 01 Oct 2026 00:00:00 GMT</pubDate><category>TinyML</category><category>Edge AI</category><category>Radar UWB</category><category>Quantizzazione</category><category>CNN</category><category>TensorFlow Lite</category></item><item><title>Agente QA PoliMillionaire: RAG su ricerca web e LLM locale</title><link>https://devisnishku.com/progetti/polimillionaire-qa-agent/</link><guid isPermaLink="true">https://devisnishku.com/progetti/polimillionaire-qa-agent/</guid><description>RAG su ricerca web con Qwen2.5-7B a 4 bit per un quiz a tempo, in testo e voce: 1 milione di dollari raggiunto in testo in ogni categoria.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>RAG</category><category>LLM open-weights</category><category>Ricerca web</category><category>Reranking</category><category>Quantizzazione</category><category>NLP</category></item><item><title>Classificazione di serie temporali multivariate con Conv1D e GRU</title><link>https://devisnishku.com/progetti/multivariate-time-series-classification/</link><guid isPermaLink="true">https://devisnishku.com/progetti/multivariate-time-series-classification/</guid><description>Classificazione di serie temporali multivariate in PyTorch, challenge di un corso del Politecnico di Milano: F1 del 95,5% sulla leaderboard Kaggle.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>Serie temporali</category><category>Deep learning</category><category>PyTorch</category><category>GRU</category><category>Classi sbilanciate</category><category>Kaggle</category></item><item><title>Continuous LSTM e cPNN in CapyMOA per il continual learning</title><link>https://devisnishku.com/progetti/continuous-lstm-cpnn-capymoa/</link><guid isPermaLink="true">https://devisnishku.com/progetti/continuous-lstm-cpnn-capymoa/</guid><description>cLSTM e cPNN come classificatori CapyMOA su uno stream con 7 concept drift: Kappa medio più alto per il cPNN in 2 concetti, per il cLSTM in 5.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>Streaming continual learning</category><category>Concept drift</category><category>CapyMOA</category><category>PyTorch</category><category>LSTM</category></item><item><title>Pipeline di qualità dei dati sul Global Shark Attack File</title><link>https://devisnishku.com/progetti/shark-attacks-data-quality-pipeline/</link><guid isPermaLink="true">https://devisnishku.com/progetti/shark-attacks-data-quality-pipeline/</guid><description>Pipeline in pandas sul Global Shark Attack File: da 6.094 a 5.980 righe, completezza dal 91,54% al 100%, 91 duplicati rimossi con record linkage.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>Qualità dei dati</category><category>Data cleaning</category><category>Record linkage</category><category>Deduplicazione</category><category>pandas</category><category>Python</category></item><item><title>Streaming Continual Learning: tesi sulle strategie architetturali</title><link>https://devisnishku.com/progetti/master-thesis-streaming-continual-learning/</link><guid isPermaLink="true">https://devisnishku.com/progetti/master-thesis-streaming-continual-learning/</guid><description>Tesi magistrale appena avviata al Politecnico di Milano sulle strategie architetturali di deep learning per lo Streaming Continual Learning.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>Streaming Continual Learning</category><category>Concept drift</category><category>Deep learning</category><category>Reti neurali ricorrenti</category><category>Tesi magistrale</category></item></channel></rss>