Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc.
Modèles de séquence
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There are 3 modules in this course
Découvrez les réseaux neuronaux récurrents. Ce type de modèle s’est avéré extrêmement performant sur les données temporelles. Il comporte plusieurs variantes, y compris les LSTM, les GRU et les RNN bidirectionnels, que vous allez découvrir dans cette section.
What's included
12 videos2 readings1 quiz3 programming assignments3 ungraded labs
Le traitement du langage naturel avec l'apprentissage profond est une combinaison importante. En utilisant des représentations de vecteurs de mots et des couches de prolongements, vous pouvez former des réseaux neuronaux récurrents avec des performances exceptionnelles, dans une grande variété de secteurs. Des exemples d’applications sont l’analyse de sentiments, la reconnaissance d’entités nommées et la traduction automatique.
What's included
10 videos1 reading1 quiz2 programming assignments2 ungraded labs
Les modèles de séquence peuvent être améliorés à l’aide d’un mécanisme d’attention. Cet algorithme aidera votre modèle à comprendre où celui-ci doit focaliser son attention, compte tenu d’une séquence d’entrées. Cette semaine, vous apprendrez également à reconnaître la parole et à gérer les données audio.
What's included
11 videos3 readings1 quiz2 programming assignments2 ungraded labs
Offered by
Recommended if you're interested in Machine Learning
DeepLearning.AI
DeepLearning.AI
DeepLearning.AI
Universidade de São Paulo
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