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Le Machine Learning : EnjoyDigitAll vous explique (presque) tout en une infographie !

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Le machine learning est ce qui permet à l’Intelligence Artificielle d’être “intelligente”, c’est-à-dire d’apprendre, et non de se limiter à être une puissante source de calcul.
L’idée est de récupérer de la data et de la faire passer dans un algorithme pour que celui-ci s’en nourrisse et soit capable de modifier ses processus de gestion de l’information pour
générer des prises de décision. L’intelligence artificielle est maintenant capable d’apprendre
et de s’adapter.

Cas d'usage et bases de connaissance !

Published in: Data & Analytics
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Le Machine Learning : EnjoyDigitAll vous explique (presque) tout en une infographie !

  1. 1. by LES DIFFÉRENTES TECHNIQUES D’APPRENTISSAGE ALGORITHMES & MACHINE LEARNING LE MACHINE LEARNING ET L’ENTRERPRISE En amont, les données sont dotées d’une caractéristique cible par des humains. Cette technique d’apprentissage nécessite que des instructeurs apprennent à la machine les résultats qu’elle est censée fournir. APPRENTISSAGE SUPERVISÉ APPRENTISSAGE PAR RENFORCEMENT L’idée est de reproduire l’apprentissage par la technique essai-erreur. L’algorithme fait une observation d’après des données qui lui sont fournies, de laquelle s’ensuit une action sur l'environnement qui à son tour produit de la donnée qui va nourrir l’algorithme. Il reçoit alors des récompenses quand il a juste et des pénalités quand il a faux, à partir de quoi il décide ou non de reproduire une action dans le futur. APPRENTISSAGE PROFOND Ou Deep Learning sert à apprendre de façon autonome à partir de data non structurées, c’est-à-dire qui ne peuvent pas être représentées dans un tableau. De cette manière, l’IA ne serait pas limitée à une tâche spécifique pour laquelle elle serait programmée. Cet apprentissage regroupe et est généré par une association des 4 techniques précédentes. Par exemple, notre intelligence artificielle a maintenant appris comment déterminer si une image représente un chat ou non. On lui demande alors d’apprendre à reconnaître des crocodiles. L’IA va se servir de ce qu’elle a appris en termes de techniques de reconnaissance, et sera par la suite bien plus performante dans sa capacité à reconnaître des chats. sont déterministes, leurs critères de fonctionnement sont explicitement définis par ceux qui les mettent en œuvre. On constate aussi aujourd’hui qu’à partir du deep learning, les chercheurs obtiennent parfois des résultats inconnus jusque-là, à partir desquels ils conduisent une recherche permettant de comprendre ces nouvelles observations. C’est ce qu’on appelle une “recherche inversée”, qui permet d’accéder à des découvertes qui n’auraient pas été envisageables par la construction classique d’hypothèses ou d’essais prospectifs. requiert presque toujours de faire des choix de méthode et des arbitrages manuels, avec un risque de finir avec des données incomplètes, non à jour, ou biaisées. Les spécialistes du domaine doivent donc rester vigilant et s’efforcer d’être représentatif dans le choix des données fournies aux algorithmes ! À titre d’exemple, l’IA de Microsoft @TayAndYou qui s’est mise à tenir des propos racistes sur Twitter en moins de 24h… LES ALGORITHMES CLASSIQUES LE MACHINE LEARNING sont probabilistes. S’ils constituent une technologie bien plus puissante que les algorithmes classiques, leurs résultats sont mouvants et dépendent à chaque instant de la base d’apprentissage qui leur a été fournie et qui évolue elle-même au fur et à mesure de leur utilisation. LES ALGORITHMES APPRENANTS Le machine learning est ce qui permet à l’Intelligence Artificielle d’être “intelligente”, c’est-à-dire d’apprendre, et non de se limiter à être une puissante soure de calcul. L’idée est de récupérer de la data et de la faire passer dans un algorithme pour que celui-ci s’en nourrisse et soit capable de modifier ses processus de gestion de l’information pour générer des prises de décision. L’intelligence artificielle est maintenant capable d’apprendre et de s’adapter. 01010101 11100110 10101101 1 0 10 1 1 1 1 0 0 LEARNING MACHINE CHAT SOURIS LAPIN CHAT APPRENTISSAGE NON-SUPERVISÉ Les données sont dépourvues de caractéristiques cibles, et c’est l’algorithme qui va établir sa propre classification, souvent de manière statistique. Cette technique d’apprentissage le rend libre d’évoluer vers n’importe quel état final. APPRENTISSAGE PAR TRANSFERT On fournit un certain type de données à l’algorithme, pour l’entraîner à résoudre un problème. Puis on lui donne à résoudre un problème similaire, mais avec des caractéristiques différentes. C’est là qu’il apprend, et les connaissances acquises par ce processus vont l’aider à mieux résoudre le problème initial ! Il est très utilisé dans la reconnaissance visuelle : par exemple, on fournit à l’IA des milliers d’images, en lui disant s’il s’agit ou non de chats. L’IA élabore ensuite elle-même, par confrontation de toutes ces images qualifiées, les critères sur lesquels elle s'appuiera pour reconnaître des chats dans les images qui lui seront ultérieurement soumises. Designed by by Sources : Les usages de l'intelligence artificielle, Olivier Ezratty • Statistiques : Forbes Donner un sens à l'intelligence artificielle pour une stratégie nationale et européenne, Cédric Villani SUIVEZ-NOUS Dans le domaine bancaire, la vente de nouveaux produits est augmentée de 10% et la perte de clientèle est réduite de 20%, tout en augmentant la satisfaction client. 1 0 10 1 1 1 1 1 0 0 0 80% ...de data non structurée sur lequel les entreprises s’appuient pour prendre leurs décisions au quotidien – d’où la nécessité d’algorithmes performant pour pouvoir les traiter. 76% ...des entreprises disent avoir augmenté leur chiffre d’affaire grâce à la mise en place de systèmes de machine learning. 40% ...des entreprises utilisent déjà le machine learning pour augmenter les ventes et les performances marketing.

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