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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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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

activation function 145

exponential linear unit (ELU) 146

hyperbolic tangent (tanh) function 146

layer 147

Leaky ReLU 146

rectified linear unit (ReLU) function 146

sigmoid function 145

softmax function 146

AdaBoost 74

advanced LangChain configurations and pipelines, applying

paid LLM (OpenAI's GPT) and free LLM (from Hugging Face), selecting between 222, 223

QA chain, creating 223

required Python libraries, installing 222

advanced methods, using with chains

element of memory, inserting 225-227

LLM, used for answering questions 224

output structure, requesting 224

AllenNLP 91

Amatriain, Xavier

insights, on NLP and LLMs 281, 294, 295

Amazon Machine Images (AMIs) 215

Amazon SageMaker 215

anomaly detection 81

artificial intelligence...

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