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Natural Language Processing with Sequence Models

Approx. 22 hours to complete

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Course Summary

This course will teach you how to build sequence models in natural language processing, including recurrent neural networks, LSTM and GRU networks, and attention models.

Key Learning Points

Learn the basics of sequence models in NLP
Understand how to build and train recurrent neural networks
Explore more advanced models like LSTM and GRU networks
Discover how to use attention models to improve performance

Job Positions & Salaries of people who have taken this course might have

- USA: $114,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $114,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $112,000
- India: ₹1,200,000
- Spain: €42,000
- USA: $114,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $112,000
- India: ₹1,200,000
- Spain: €42,000
- USA: $117,000
- India: ₹1,400,000
- Spain: €50,000

Learning Outcomes

Build and train recurrent neural networks for sequence modeling
Implement more advanced models like LSTM and GRU networks
Use attention models to improve performance on NLP tasks

Prerequisites or good to have knowledge before taking this course

Familiarity with Python and TensorFlow
Basic understanding of neural networks

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Programming assignments
Quizzes

Similar Courses

Advanced NLP with Spacy
Applied Machine Learning
Deep Learning

Related Education Paths

Notable People in This Field

Sebastian Ruder
Yoav Goldberg
Jason Weston

Related Books

Description

In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. Please make sure that you’ve completed Course 2 and are familiar with the basics of TensorFlow. If you’d like to prepare additionally, you can take Course 1: Neural Networks and Deep Learning of the Deep Learning Specialization. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Knowledge

Create word embeddings, then train a neural network on them to perform sentiment analysis of tweets
Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model
Train a recurrent neural network to extract important information from text, using named entity recognition (NER) and LSTMs with linear layers
Use a Siamese network to compare questions in a text and identify duplicates: questions that are worded differently but have the same meaning

Outline

Neural Networks for Sentiment Analysis
Course 3 Introduction
Neural Networks for Sentiment Analysis
Trax: Neural Networks
Why we recommend Trax
Trax: Layers
Dense and ReLU Layers
Serial Layer
Other Layers
Training
Connect with your mentors and fellow learners on Slack!
Neural Networks for Sentiment Analysis
Trax: Neural Networks
Reading: (Optional) Trax and JAX, docs and code
Trax: Layers
Dense and ReLU layer
Serial Layer
Other Layers
Training
How to Refresh your Workspace

Recurrent Neural Networks for Language Modeling
Traditional Language models
Recurrent Neural Networks
Applications of RNNs
Math in Simple RNNs
Cost Function for RNNs
Implementation Note
Gated Recurrent Units
Deep and Bi-directional RNNs
Traditional Language models
Recurrent Neural Networks
Application of RNNs
Math in Simple RNNs
Cost Function for RNNs
Implementation Note
Gated Recurrent Units
Deep and Bi-directional RNNs

LSTMs and Named Entity Recognition
RNNs and Vanishing Gradients
Introduction to LSTMs
LSTM Architecture
Introduction to Named Entity Recognition
Training NERs: Data Processing
Computing Accuracy
RNNs and Vanishing Gradients
(Optional) Intro to optimization in deep learning: Gradient Descent
Introduction to LSTMs
(Optional) Understanding LSTMs
LSTM Architecture
Introduction to Named Entity Recognition
LSTM equations (Optional)
Training NERs: Data Processing
Long Short-Term Memory (Deep Learning Specialization C5)
Computing Accuracy

Siamese Networks
Siamese Networks
Architecture
Cost Function
Triplets
Computing The Cost I
Computing The Cost II
One Shot Learning
Training / Testing
Siamese Network
Architecture
Cost Function
Triplets
Computing the Cost I
Computing the Cost II
One Shot Learning
Training / Testing
Acknowledgments

Summary of User Reviews

Discover how to use sequence models in natural language processing with this comprehensive Coursera course. Students rave about the in-depth content and engaging instructors, giving this course an overall positive rating. One key aspect that many users appreciate is the practical application of the material, which makes it easy to understand and implement.

Pros from User Reviews

The course covers a wide range of topics in sequence models and NLP, offering an in-depth understanding of the subject matter
The instructors provide clear explanations and offer practical examples that make the course engaging and easy to follow
The course is well-structured and organized, making it easy to navigate and follow along with the material
The hands-on assignments and quizzes allow you to apply what you've learned in practice, reinforcing your understanding of the concepts
The course is highly relevant for anyone interested in natural language processing, machine learning, or data science

Cons from User Reviews

Some users find the course challenging and would have appreciated more guidance from the instructors
The course is quite technical and may not be suitable for beginners or those without a strong background in math and programming
Some users feel that the course could benefit from more interactive components, such as live sessions or forums for discussion and collaboration

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