- Represent input words are x<1>


- Output to match is y<1>


- Tx denotes length of X.


- Ty denotes length of y.


Sequence Models: Notation

Each word is represented as a vector, with value 1 for the word and 0 for all other words in the dictionary (similar to one-hot encoding)

Representing  Words

The task of predicting what word/letter comes next, unlike the FNN and CNN, in sequence modeling the current output is dependent on the previous a=input and the length of the input is not fixed.


Why sequence models? 

Given a sequence of words, what is the most probable next word/sentence?

Language Model

Sequence labeling is a machine learning methodology used across many Natural Language Processing (NLP) applications. The core idea is to assign a specific class or tag to each token within a given input sequence. The resulting sequence of labels can then be interpreted to extract linguistic annotations. Common examples of this technique include Part-of-Speech (POS) tagging, where each word is labeled with its grammatical role, and Named Entity Recognition (NER), where tokens are tagged to identify entities like names or locations.

Sequence Labeling

https://www.coursera.org/learn/nlp-sequence-models

University of Michigan - Ann Arbor

 Coursera offers a five course specialization that teaches the foundations of Deep Learning in terms of understanding "how to build neural networks" and learning "how to lead successful machine learning projects". The specialization consists of the following courses:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models


https://www.coursera.org/specializations/deep-learning

Deep Learning Specialization

https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning

Neural Networks and Deep Learning

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning

Structuring Machine Learning Projects

https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning

Convolutional Neural Networks

Sequence Models

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

https://www.coursera.org/professional-certificates/ibm-data-science
Beginner course

IBM Data Science Professional Certificate

https://www.coursera.org/specializations/jhu-data-science
ten course specialization on coursera offered by JHU staff

Learn Before

Related

Learn After