Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior 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)

Technical requirements

For this chapter, you are expected to possess a solid foundation in machine learning (ML) concepts, particularly in the areas of Transformers and reinforcement learning. An understanding of Transformer-based models, which underpin many of today’s LLMs, is vital. This includes familiarity with concepts such as self-attention mechanisms, positional encoding, and the structure of encoder-decoder architectures.

Knowledge of reinforcement learning principles is also essential, as we will delve into the application of RLHF in the fine-tuning of LMs. Familiarity with concepts such as policy gradients, reward functions, and Q-learning will greatly enhance your comprehension of this content.

Lastly, coding proficiency, specifically in Python, is crucial. This is because many of the concepts will be demonstrated and explored through the lens of programming. Experience with PyTorch or TensorFlow, popular ML libraries, and Hugging Face’s Transformers...