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Sequence Models

deeplearning.ai and Stanford University via Coursera

3 Reviews 880 students interested
  • Provider Coursera
  • Subject Artificial Intelligence
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 3 weeks long
  • Learn more about MOOCs

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Overview

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This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

You will:
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
- Be able to apply sequence models to natural language problems, including text synthesis.
- Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

This is the fifth and final course of the Deep Learning Specialization.

deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

Taught by

Andrew Ng

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Reviews for Coursera's Sequence Models
5.0 Based on 3 reviews

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  • 1
Raivis J
5.0 a month ago
Raivis completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
This is the hardest course in the specialisation, and may take some extra effort. For practical assignments I recommend getting familiar with Keras syntax and workflow, as here there is little hand-holding here,. the focus is on actual model architecture and algorithms.
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Wichaiditsornpon@gmail.com W
5.0 a year ago
by Wichaiditsornpon@gmail.com completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
Prof Andrew do a great work as usual i never seen better explanation for RNN, Prof Andrew break down such a complex theory stuff to small piece of easy to follow and understandable stuff
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Anonymous
5.0 a year ago
Anonymous completed this course.
Best RNN course out there. Great explanation, amazing practical examples and interesting quizes. Well prepared. Good to take earlier courses in the specialization.
Was this review helpful to you? Yes
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