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Linguaggi di programmazione
Tutorial su Python, C, C++, Java, JavaScript, TypeScript, Go, R e Haskell — dalla sintassi di base alle tecniche avanzate.
Sviluppo Web
Guide e progetti su HTML5, React, Angular, Next.js, Vite e sviluppo frontend moderno.
Sviluppo Backend
Sviluppo lato server con Node.js, NestJS, Spring Boot, Python, socket, Nginx e integrazione dei pagamenti.
Algoritmi e strutture dati
Ordinamento, alberi, hashing, algoritmi sui grafi, KMP, esercizi LeetCode e analisi degli algoritmi.
IA e Machine Learning
Machine learning, generazione aumentata dal recupero (RAG), applicazioni IA e web crawling.
Database
Progettazione di database SQL e NoSQL, query, ottimizzazione ed esercizi pratici.
Fondamenti di informatica
Organizzazione dei computer, sistemi operativi, fondamenti di Linux e teoria informatica di base.
Hardware e sistemi embedded
Arduino, RISC-V, MSP430, PLC, driver Linux e ingegneria dei sistemi embedded.
Robotica e sistemi autonomi
ROS 2, robot umanoidi, droni e sistemi di veicoli autonomi.
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Reti di computer, TCP/IP, pipeline CI/CD e pratica DevOps.
Sviluppo mobile
Tutorial di sviluppo di app Android e iOS.
Finanza quantitativa e Blockchain
Finanza quantitativa, sistemi di trading e tecnologia blockchain.
Progetti e casi di studio
Progetti reali: microservizi per gateway di pagamento, app full-stack, backend Django, sistemi RAG e altro.
Ultimi articoli
Comprehensive Machine Learning & Deep Learning Curriculum
A complete set of professional, textbookquality teaching PDFs covering a depthfirst path from mathematical foundations to production deep learning — written for someone who already codes well but wants real understanding
Leggi l'articolo →Preface: how to use this book
For most of the 1990s and 2000s, if you wanted the best off-the-shelf classifier, you reached for a Support Vector Machine. SVMs combined a beautiful geometric idea — separate the classes with the — with deep optimizatio
Leggi l'articolo →Formulas, Kernels, and Hyperparameter Reference
A compact reference for the formulas, kernels, and knobs used throughout the book.
Leggi l'articolo →What Are Support Vector Machines?
Imagine two groups of points on a page and a ruler you must lay down to separate them. Many positions work — but which is ? A Support Vector Machine answers: the line that leaves the between the groups. That single insti
Leggi l'articolo →The Maximal Margin Classifier
Now we make the widest-street idea precise. For data that be perfectly separated by a line, the maximal margin classifier (the hard-margin SVM) is the cleanest version of the story: a little geometry turns ``make the str
Leggi l'articolo →Soft Margins and Slack
Real data overlaps, contains noise, and is rarely perfectly separable. The — the model people actually use — relaxes the hard constraints by allowing a controlled budget of margin violations. This single change makes SVM
Leggi l'articolo →Hinge Loss and the Primal Problem
We ended the last chapter with a striking reformulation: the soft-margin SVM is just L_2 regularization plus a special loss. That loss is the , and viewing the SVM as ``minimize regularized hinge loss'' (the problem) dem
Leggi l'articolo →Lagrangian Duality and the KKT Conditions
The primal view trains SVMs by gradient descent, but it hides two treasures: support vectors appear, and the kernel trick becomes possible. Both emerge when we rewrite the SVM through . This is the most mathematical chap
Leggi l'articolo →The Kernel Trick
Here is the idea that turned a linear classifier into one of the most powerful tools in machine learning. A linear SVM can only draw straight boundaries — useless for data shaped like rings or spirals. The lets the very
Leggi l'articolo →