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.
This course is part of the Deep Learning Specialization
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Offered By
Deep Learning SpecializationDeepLearning.AIAbout this Course
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Course 5 of 5 in the
Intermediate Level
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Approx. 42 hours to complete
English
Subtitles: English, Korean, Arabic
Skills you will gain
- Natural Language Processing
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Recurrent Neural Network
- Attention Models
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 5 of 5 in the
Intermediate Level
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Approx. 42 hours to complete
English
Subtitles: English, Korean, Arabic
Offered by
DeepLearning.AI
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Syllabus - What you will learn from this course
14 hours to complete
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,
14 hours to complete
12 videos (Total 112 min), 4 readings, 6 quizzes
9 hours to complete
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.
9 hours to complete
10 videos (Total 99 min), 1 reading, 4 quizzes
10 hours to complete
Sequence Models & Attention Mechanism
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.
10 hours to complete
10 videos (Total 98 min), 3 readings, 4 quizzes
9 hours to complete
Transformer Network
9 hours to complete
5 videos (Total 42 min), 4 readings, 3 quizzes
Reviews
- 5 stars83.62%
- 4 stars13.03%
- 3 stars2.57%
- 2 stars0.47%
- 1 star0.28%
TOP REVIEWS FROM SEQUENCE MODELS
by MHApr 21, 2020
Very good. I have no complaints. I though instruction was very clear. Assignments were very helpful and challenging enough that I learned something, but not so challenging that I got stuck too often.
by AMJul 4, 2018
Excellent course! This course extensively covers all of the relevant areas of NLP with a strong practical element allowing you to applying Deep Learning for Sequence Models in real-world scenarios.
by JRMay 25, 2019
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
by JSJul 12, 2020
brilliant course, great quality instruction from Andrew Ng. The only faults are that some of the labs have not been supervised properly being a but buggy and a couple of later lectures were very dry.
About the Deep Learning Specialization
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
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