Bibliothèque

Voici notre sélection pour la bibliothèque sur les sciences de l’informatique et mathématiques

bibliothèque

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

Quand la machine apprend

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

The C Programming Language

Programmer en Python

Pratiques de programmation :

Design Patterns

Clean Code

The Pragmatic Programmer

UML Distilled: A Brief Guide

UML 2.5 par la pratique

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

Introduction to Graph Theory

Algorithmique :

Introduction to Algorithms

Algorithms

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

Algorithms for Optimization

Convex Optimization

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 Chain Monte Carlo

Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues

Chaînes de Markov – Cours et exercices corrigés

Markov Chains

Introduction to Probability Models

Logique :

Logique mathématique – Tome 1

Logique mathématique – Tome 2

 

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