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Natural Language Processing with Attention Models

Approx. 31 hours to complete

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Course Summary

This course teaches various attention models in natural language processing (NLP), including self-attention, multi-headed attention, and more. Students will learn how to implement these models in practice and apply them to real-world NLP problems.

Key Learning Points

Learn various attention models in NLP
Implement attention models in practice
Apply models to real-world NLP problems

Learning Outcomes

Understand various attention models in NLP
Implement attention models in practice
Apply models to real-world NLP problems

Prerequisites or good to have knowledge before taking this course

Basic knowledge of NLP and machine learning
Proficiency in Python programming

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Assignments and quizzes

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Description

In Course 4 of the Natural Language Processing Specialization, offered by DeepLearning.AI, you will:

a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. This course is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them. 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! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course. 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.

Outline

Neural Machine Translation
Course 4 Introduction
Seq2seq
Alignment
Attention
Setup for Machine Translation
Training an NMT with Attention
Evaluation for Machine Translation
Sampling and Decoding
Andrew Ng with Oren Etzioni
Connect with your mentors and fellow learners on Slack!
Background on seq2seq
(Optional): The Real Meaning of Ich Bin ein Berliner
Attention
Training an NMT with Attention
(Optional) What is Teacher Forcing?
Evaluation for Machine Translation
Sampling and Decoding
Content Resource
How to Refresh your Workspace

Text Summarization
Transformers vs RNNs
Transformer Applications
Dot-Product Attention
Causal Attention
Multi-head Attention
Transformer Decoder
Transformer Summarizer
Transformers vs RNNs
Transformer Applications
Dot-Product Attention
Causal Attention
Multi-head Attention
Transformer Decoder
Transformer Summarizer
Content Resource

Question Answering
Week 3 Overview
Transfer Learning in NLP
ELMo, GPT, BERT, T5
Bidirectional Encoder Representations from Transformers (BERT)
BERT Objective
Fine tuning BERT
Transformer: T5
Multi-Task Training Strategy
GLUE Benchmark
Question Answering
Week 3 Overview
Transfer Learning in NLP
ELMo, GPT, BERT, T5
Bidirectional Encoder Representations from Transformers (BERT)
BERT Objective
Fine tuning BERT
Transformer T5
Multi-Task Training Strategy
GLUE Benchmark
Question Answering
Content Resource

Chatbot
Tasks with Long Sequences
Transformer Complexity
LSH Attention
Motivation for Reversible Layers: Memory!
Reversible Residual Layers
Reformer
Andrew Ng with Quoc Le
Tasks with Long Sequences
Optional AI Storytelling
Transformer Complexity
LSH Attention
Optional KNN & LSH Review
Motivation for Reversible Layers: Memory!
Reversible Residual Layers
Reformer
Optional Transformers beyond NLP
Acknowledgments
References
(Optional) Opportunity to Mentor Other Learners

Summary of User Reviews

Explore attention models in NLP and learn how to apply them in real-world scenarios. The course has received positive reviews from users who found it informative and well-structured.

Key Aspect Users Liked About This Course

Many users found the course content to be comprehensive and informative.

Pros from User Reviews

The course covers a range of attention models and their applications in NLP
The course is well-structured and easy to follow
The instructors provide clear explanations and examples
The assignments are challenging but rewarding
The course provides practical knowledge that can be applied in real-world scenarios

Cons from User Reviews

The course may be too technical for beginners
Some users found the pace of the course to be slow
The course does not cover advanced topics in depth
The course may not be suitable for those looking for a quick overview of attention models in NLP
The course requires a significant time commitment

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