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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Gain insight into a topic and learn the fundamentals.
4.8
(30,441 reviews)
Intermediate level
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Flexible schedule
Approx. 37 hours
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Assessments
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 videos3 readings1 assignment2 programming assignments
What's included
5 videos5 readings1 assignment1 programming assignment3 ungraded labs
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4.9 (2,647 ratings)Offered by
Recommended if you're interested in Machine Learning
University of Pennsylvania
DeepLearning.AI
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4.8
30,441 reviews
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Showing 3 of 30441
BA
4
Reviewed on Jan 14, 2019
The material itself is very informative and useful. But I have to give "just" 4 stars because, the training videos have to be edited better and there were a few mistakes in the programing exercises.
EM
5
Reviewed on Jun 12, 2019
A really joyful introduction in the Sequence Models, such as RNNs, LSTM etc. Sometimes the assignments got a little hard and with patience and help from forums, it gets achievable! Thanks again! :D
NM
5
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.
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