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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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A winning synergy – the coming together of NLP and ML

ML is a subfield of AI that involves training algorithms to learn from data, allowing them to make predictions or decisions without those being explicitly programmed. ML is driving advancements in so many different fields, such as computer vision, voice recognition, and, of course, NLP.

Diving a little more into the specific techniques of ML, a particular technique used in NLP is statistical language modeling, which involves training algorithms on large text corpora to predict the likelihood of a given sequence of words. This is used in a wide range of applications, such as speech recognition, machine translation, and text generation.

Another essential technique is DL, which is a subfield of ML that involves training artificial neural networks on large amounts of data. DL models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been shown to be adequate for NLP tasks such as language understanding, text summarization, and sentiment analysis.

Figure 1.2 portrays the relationship between AI, ML, DL, and NLP:

Figure 1.2 – The relationship between the different disciplines

Figure 1.2 – The relationship between the different disciplines

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