Course Summary
This course on NLP Sequence Models teaches students how to build models for natural language processing tasks such as speech recognition, machine translation, and sentiment analysis.Key Learning Points
- Learn to build and train models for natural language processing tasks using sequence models
- Understand how to handle complex data such as audio, text, and images
- Get hands-on experience with building models using TensorFlow and Keras
Related Topics for further study
Learning Outcomes
- Understand the basics of sequence models for natural language processing
- Learn how to preprocess different types of data for NLP
- Build and train models using TensorFlow and Keras
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming
- Familiarity with Python
- Knowledge of machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Natural Language Processing with Deep Learning
- Applied AI with Deep Learning
Related Education Paths
Notable People in This Field
- Yann LeCun
- Geoffrey Hinton
Related Books
Description
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.
Outline
- Recurrent Neural Networks
- Why Sequence Models?
- Notation
- Recurrent Neural Network Model
- Backpropagation Through Time
- Different Types of RNNs
- Language Model and Sequence Generation
- Sampling Novel Sequences
- Vanishing Gradients with RNNs
- Gated Recurrent Unit (GRU)
- Long Short Term Memory (LSTM)
- Bidirectional RNN
- Deep RNNs
- Connect with your Mentors and Fellow Learners on Discourse!
- Gated Recurrent Unit (GRU) *CORRECTION*
- Long Short Term Memory (LSTM) *CORRECTION*
- Lectures in PDF
- How to Download your Notebook
- H​ow to Refresh your Workspace
- Recurrent Neural Networks
- Natural Language Processing & Word Embeddings
- Word Representation
- Using Word Embeddings
- Properties of Word Embeddings
- Embedding Matrix
- Learning Word Embeddings
- Word2Vec
- Negative Sampling
- GloVe Word Vectors
- Sentiment Classification
- Debiasing Word Embeddings
- GloVe Word Vectors *CORRECTION*
- Lectures in PDF
- Natural Language Processing & Word Embeddings
- Sequence Models & Attention Mechanism
- Basic Models
- Picking the Most Likely Sentence
- Beam Search
- Refinements to Beam Search
- Error Analysis in Beam Search
- Bleu Score (Optional)
- Attention Model Intuition
- Attention Model
- Speech Recognition
- Trigger Word Detection
- Bleu Score *CORRECTION*
- Corrections
- Lectures in PDF
- Instructions If You Are Unable to Open Your Notebook
- Sequence Models & Attention Mechanism
- Transformer Network
- Transformer Network Intuition
- Self-Attention
- Multi-Head Attention
- Transformer Network
- Conclusion and Thank You!
- Lectures in PDF
- Transformers using Trax Library
- References
- Acknowledgments
- (Optional) Opportunity to Mentor Other Learners
- Transformers
Summary of User Reviews
The NLP Sequence Models course on Coursera has received positive reviews from many users. It is highly recommended for anyone interested in natural language processing. One key aspect that users found good was the quality and depth of the course content.Pros from User Reviews
- The course content is comprehensive and detailed.
- The instructor is knowledgeable and engaging.
- The assignments and quizzes are helpful for reinforcing concepts.
- The course is well-structured and easy to follow.
- The practical applications of the concepts are useful and relevant.
Cons from User Reviews
- Some users found the course to be too technical and challenging.
- The pace of the course may be too fast for some learners.
- The course may require prior knowledge of programming and machine learning.
- The peer review process for assignments can be time-consuming.
- Some users felt that the course could have provided more hands-on practice.
Andrew NgTop Instructor
4.8 Raiting
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