Master the art of sequence modeling with the Modèles de séquence course, offered by DeepLearning.AI. This comprehensive training delves into the construction and application of models for natural language, audio, and various sequence data. Through engaging modules, you will explore recurrent neural networks (RNN) and their variations, delve into word representation, and understand attention mechanisms. This course is the final component of the Deep Learning Specialization, providing you with cutting-edge industry-relevant projects.
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Enhance your expertise in deep learning and gain practical skills in building and training sequence models with this dynamic course.
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Modèles de séquence consists of five modules that cover recurrent neural networks, natural language processing, word embeddings, and attention mechanisms. Delve into advanced techniques for sequence modeling and practical applications.
Module 1: This module provides a detailed exploration of recurrent neural networks, encompassing their architecture, training, and applications. You will delve into the fundamentals of RNN, including GRU and LSTM variants, and gain practical experience through hands-on projects such as text synthesis and music modeling.
Module 2: Gain expertise in natural language processing and word embeddings, including techniques such as Word2Vec and GloVe. Explore the use of word vectors for sentiment analysis and debiasing, and apply your skills to projects like Emojify and bias reduction in word embeddings.
Module 3: Delve into advanced sequence modeling with attention mechanisms. Understand the intuition behind attention models and their applications in tasks like speech recognition and neural machine translation. Dive into the complexities of beam search and error analysis, refining your skills in sequence modeling.
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