Contenus
ToggleNotre sélection de livres pour la bibliothèque parfaite !
Voici notre sélection pour la bibliothèque sur les sciences de l’informatique et mathématiques
Deep learning et réseaux de neurones
Mathematics for Machine Learning
Linear Algebra and Optimization for Machine Learning
Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms
Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks
Clever Algorithms: Nature-Inspired Programming Recipes
Approaching (Almost) Any Machine Learning Problem
The Hundred-Page Machine Learning Book
Mathematics for Machine Learning
Artificial Intelligence: A Modern Approach, Global Edition
Langage de programmation :
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow
Julia Programming for Operations Research
Dynamic Programming for Coding Interviews
Pratiques de programmation :
Clean Architecture: A Craftsman’s Guide to Software Structure and Design
Théories informatiques :
Introduction to Automata Theory, Languages, and Computation
Theory of Games and Economic Behavior
Game Theory 101: The Complete Textbook
Algorithmique :
Algorithms Illuminated: Part 1: The Basics
Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures
Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming
Algorithms Illuminated (Part 4): Algorithms for NP-Hard Problems
Recherche opérationnelle et optimisation :
Julia Programming for Operations Research
Combinatorial Optimization: Algorithms and Complexity
Optimization Techniques in Operation Research
Optimization Techniques in Operation Research
Théorie de la décision :
Des mathématiques pour les sciences – Concepts, méthodes et techniques pour la modélisation
An Introduction to Decision Theory
Theory of Decision under Uncertainty
Decision Theory: Principles and Approaches
Stochastique :
Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues
Chaînes de Markov – Cours et exercices corrigés
Introduction to Probability Models
Logique :