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In the fifth course of the deep learning specialization, you will become familiar with sequence models and their exciting applications, such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and plus. By the end, you will be able to build and train Recurrent Neural Networks (RNN) and commonly used variants like GRU and LSTM; apply RNN to character-level language modeling; gain experience with natural language processing and Word Embed
In the fifth course of the deep learning specialization, you will become familiar with sequence models and their exciting applications, such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and plus.
By the end, you will be able to build and train Recurrent Neural Networks (RNN) and commonly used variants like GRU and LSTM; apply RNN to character-level language modeling; gain experience with natural language processing and word embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks like NER and Question Answering.
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of cutting-edge AI technology.
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Learn about recurrent neural networks, a type of model that works extremely well on temporal data, and several of its variants, including bidirectional LSTMs, GRUs, and RNNs,
Natural language processing with deep learning is a powerful combination. Using word vector representations and layer embeddings, train recurrent neural networks with exceptional performance in a wide variety of applications, including sentiment analysis, named entity recognition, and neural machine translation.
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then explore speech recognition and how to handle audio data.
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