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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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Summary

In this chapter, we’ve delved into the dynamic and complex world of state-of-the-art LLMs. We’ve discussed their remarkable generalization capabilities, making them versatile tools for a wide range of tasks. We also highlighted the crucial aspect of understanding complex contexts, where these models excel by grasping the nuances of language and the intricacies of various subject matters.

Additionally, we explored the paradigm of RLHF and how it is being employed to enhance LMs. RLHF leverages scalar feedback to improve LMs by mimicking human judgments, thereby helping to mitigate some of the common pitfalls encountered in NLP tasks.

We discussed technical requirements for working with these models, emphasizing the need for foundational knowledge in areas such as Transformers, reinforcement learning, and coding skills.

This chapter also touched on some prominent LMs such as GPT-4 and LLaMA, discussing their architecture, methods, and performance. We highlighted...

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