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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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Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI

Natural language processing (NLP) and large language models (LLMs) stand at the intersection of linguistics and artificial intelligence, serving as milestones in our understanding of human-computer interactions. Their story begins with basic rule-based systems, which, while innovative for their time, often stumbled due to the complex nuances and immensity of human language. The limitations of these systems highlighted the need for a shift, paving the way for the machine learning (ML) era, where data and pattern recognition prescribe the design and the models.

In this chapter, we will review key trends that have been emerging in NLP and LLMs, some of which are broad enough to capture the direction of AI as a whole. We will discuss those trends from a qualitative perspective as we aim to highlight their purpose, value, and impact. In the next sections, we’ll share our thoughts on what the future...

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