DeepLearning.AI - Natural Language Processing with Attention Models
- Offered byCoursera
Natural Language Processing with Attention Models at Coursera Overview
Duration | 31 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Natural Language Processing with Attention Models at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the Natural Language Processing Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 31 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish, Japanese
Natural Language Processing with Attention Models at Coursera Course details
- 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. ?ukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Natural Language Processing with Attention Models at Coursera Curriculum
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
Natural Language Processing with Attention Models at Coursera Admission Process
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