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.
Sequence Models
Sequence Models
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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Intermediate level
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4 weeks at 10 hours a week
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4 assignments
Taught in English
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This course is part of the Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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,
What's included
12 videos5 readings1 assignment3 programming assignments
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.
What's included
10 videos2 readings1 assignment2 programming assignments
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 deal with audio data.
What's included
10 videos2 readings1 assignment2 programming assignments
What's included
5 videos5 readings1 assignment1 programming assignment3 ungraded labs
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Showing 3 of 31227
CF
Reviewed on Feb 14, 2020
One of the best thing from this class is not only we can understand the concept of RNN, LSTM, etc, but also I also get the idea about how these technique can be used in many daily life applications
NM
Reviewed on Feb 20, 2018
Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.
GA
Reviewed on Jul 14, 2021
the assignments were a really good format for someone who hasn't learned how to derive wrt multiple variables. It made sense to have the formulas provided to introduce a context for me: a developer.
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