Analyse de sentiment avec RNNs dans Keras

Aperçu

By the end of this course, learners will be able to explain sentiment analysis concepts, apply preprocessing techniques, and construct, train, and evaluate LSTM models using Keras in Google Colab. This project-based course guides learners step by step through the complete workflow of sentiment analysis using the IMDB dataset.

Starting with setting up the Colab environment and downloading data, learners will prepare text sequences using tokenization and padding. The course then introduces the fundamentals of Long Short-Term Memory (LSTM) networks before progressing to building, training, and evaluating both simple and complex RNN models.

Learners will also practice plotting results and predicting movie review sentiments, strengthening their applied deep learning skills. What makes this course unique is its hands-on approach:

every concept is directly tied to practical implementation in Python, ensuring learners not only understand the theory but also gain real-world coding experience.

By completing this course, learners will be equipped with the ability to analyze text data, optimize RNN models, and apply deep learning for NLP tasks with confidence.

Programme

• Maîtriser l'analyse des sentiments avec les RNN
Ce module initie les apprenants à l'analyse des sentiments en utilisant les réseaux de neurones récurrents (RNN) avec Keras. Les apprenants exploreront le prétraitement des données, la gestion des séquences et la conception des modèles avec les réseaux de mémoire à long court terme (LSTM). Le module couvre la construction, l'entraînement et l'évaluation de modèles LSTM simples et complexes, permettant aux apprenants de classifier avec une grande précision les critiques de films IMDB.

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Enseigné par

EDUCBA

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Matières

Computer Science