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.
This course is part of the Deep Learning Specialization
Sequence Models
Offered By
About this Course
4.8
12,373 ratings
Skills you will gain
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Syllabus - What you will learn from this course
Week
16 hours to complete
Recurrent Neural Networks
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.
12 videos (Total 112 min), 4 quizzes
12 videos
Notation9m
Recurrent Neural Network Model16m
Backpropagation through time6m
Different types of RNNs9m
Language model and sequence generation12m
Sampling novel sequences8m
Vanishing gradients with RNNs6m
Gated Recurrent Unit (GRU)17m
Long Short Term Memory (LSTM)9m
Bidirectional RNN8m
Deep RNNs5m
1 practice exercise
Recurrent Neural Networks20m
Week
24 hours to complete
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.
10 videos (Total 102 min), 3 quizzes
10 videos
Using word embeddings9m
Properties of word embeddings11m
Embedding matrix5m
Learning word embeddings10m
Word2Vec12m
Negative Sampling11m
GloVe word vectors11m
Sentiment Classification7m
Debiasing word embeddings11m
1 practice exercise
Natural Language Processing & Word Embeddings20m
Week
35 hours to complete
Sequence models & Attention mechanism
Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.
11 videos (Total 103 min), 3 quizzes
11 videos
Picking the most likely sentence8m
Beam Search11m
Refinements to Beam Search11m
Error analysis in beam search9m
Bleu Score (optional)16m
Attention Model Intuition9m
Attention Model12m
Speech recognition8m
Trigger Word Detection5m
Conclusion and thank you2m
1 practice exercise
Sequence models & Attention mechanism20m
38%
started a new career after completing these courses
38%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Top Reviews
I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!
The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
Instructors
Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.aiAbout deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
About the Deep Learning Specialization
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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