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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)
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Advanced methods with chains

In this section, we will continue our exploration of ways one can utilize LLM pipelines. We will focus on chains.

Refer to the following notebook: Ch9_Advanced_Methods_with_Chains.ipynb. This notebook presents an evolution of a chain pipeline, as every iteration exemplifies another feature that LangChain allows us to employ.

For the sake of using minimal computational resources, memory, and time, we use OpenAI’s API. You can choose to use a free LLM instead and may do so in a similar way to how we set up the notebook from the previous example in this chapter.

The notebook starts with the basic configurations, as always, so we can skip to reviewing the notebook’s content.

Asking the LLM a general knowledge question

In this example, we want to use the LLM to tell us an answer to a simple question that would require common knowledge that a trained LLM is expected to have:

"Who are the members of Metallica. List them as...

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