Apprenez la programmation, l'IA et l'ingénierie
Des tutoriels approfondis couvrant les langages de programmation, le développement web, le machine learning, les algorithmes, la robotique et le matériel — gratuits et ouverts à tous.
Parcourir les catégories
Langages de programmation
Tutoriels Python, C, C++, Java, JavaScript, TypeScript, Go, R et Haskell — de la syntaxe de base aux techniques avancées.
Développement Web
Guides et projets HTML5, React, Angular, Next.js, Vite et développement frontend moderne.
Développement Backend
Développement côté serveur avec Node.js, NestJS, Spring Boot, Python, sockets, Nginx et intégration de paiement.
Algorithmes et structures de données
Tri, arbres, hachage, algorithmes de graphes, KMP, pratique LeetCode et analyse d'algorithmes.
IA et Machine Learning
Machine learning, génération augmentée par récupération (RAG), applications d'IA et web crawling.
Bases de données
Conception de bases de données SQL et NoSQL, requêtes, optimisation et exercices pratiques.
Fondamentaux de l'informatique
Organisation des ordinateurs, systèmes d'exploitation, bases de Linux et théorie informatique fondamentale.
Matériel et systèmes embarqués
Arduino, RISC-V, MSP430, PLC, pilotes Linux et ingénierie des systèmes embarqués.
Robotique et systèmes autonomes
ROS 2, robots humanoïdes, drones et systèmes de véhicules autonomes.
Réseaux et DevOps
Réseaux informatiques, TCP/IP, pipelines CI/CD et pratique DevOps.
Développement mobile
Tutoriels de développement d'applications Android et iOS.
Finance quantitative et Blockchain
Finance quantitative, systèmes de trading et technologie blockchain.
Projets et études de cas
Projets réels : microservices de passerelle de paiement, applications full-stack, backends Django, systèmes RAG et plus.
Derniers articles
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
Lire l'article →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
Lire l'article →Formulas, Kernels, and Hyperparameter Reference
A compact reference for the formulas, kernels, and knobs used throughout the book.
Lire l'article →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
Lire l'article →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
Lire l'article →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
Lire l'article →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
Lire l'article →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
Lire l'article →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
Lire l'article →