Sequence Models

DeepLearning.AI via Coursera

Coursera based on 30,841 ratings

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Overview

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 more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models...

Syllabus

Recurrent Neural Networks

Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,

Natural Language Processing & Word Embeddings

Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.

Sequence Models & Attention Mechanism

Augment your sequence models using an attention mechanism, an algorithm thats your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.