georgezoto/RNN-LSTM-NLP-Sequence-Models
Sequence Models repository for all projects and programming assignments of Course 5 of 5 of the Deep Learning Specialization offered on Coursera and taught by Andrew Ng, covering topics such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Natural Language Processing, Word Embeddings and Attention Model.
This project helps deep learning practitioners understand and implement sequence models for natural language processing. It takes text, audio, or other sequential data as input and produces outputs like generated text, sentiment classifications, or speech recognition results. Anyone studying or working with advanced deep learning concepts for sequence data, particularly those following Andrew Ng's Deep Learning Specialization, would find this useful.
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Use this if you are a deep learning student or researcher looking for practical implementations of RNNs, LSTMs, and attention models for NLP tasks.
Not ideal if you are looking for a plug-and-play solution without needing to understand the underlying model architectures or are not familiar with deep learning frameworks.
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Nov 10, 2019
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