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
This course is part of Deep Learning Specialization



Instructors: Andrew Ng
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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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Reviewed on Jan 25, 2019
This was a tough one. The specialization is well structured and slowly progresses in terms of complexity. Having worked on RNN, i thought I would ace the projects. Different story though at the end
Reviewed on Mar 13, 2024
Cant express how thankful I am to Andrew Ng, literally thought me from start to finish when my school didnt touch about it, learn a lot and decided to use my knowledge and apply to real world projects
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.
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