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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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. 41 hours to complete
English
Subtitles: English, Arabic
Skills you will gain
Natural Language ProcessingLong Short Term Memory (LSTM)Gated Recurrent Unit (GRU)Recurrent Neural NetworkAttention 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. 41 hours to complete
English
Subtitles: English, 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
9 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.
9 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
TOP REVIEWS FROM SEQUENCE MODELS
by AMJun 30, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
by ACJun 23, 2019
Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.
by PJApr 3, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
by NMFeb 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.
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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