step1: training set: large corpus of text

step2: tokenize text--> one-hot encoding --> add <EOS> token

step3: detecting unknown words

step4: start with the first word of the sentence and deed it as input to the RNN.

Language model with RNN

Language model alignment involves fine-tuning a pretrained language model to follow instructions and align with human intent. Systems like ChatGPT leverage this alignment to allow users to interact conversationally, enabling the model to serve as an assistant for solving complex problems, debugging code, and performing creative writing tasks.

Language Model Alignment

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

University of Michigan - Ann Arbor

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

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)

- 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? 

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.

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