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)

The challenges of training neural networks

Training neural networks is a complex task and comes with challenges during the training, such as local minima and vanishing/exploding gradients, as well as computational costs and interpretability. All challenges are explained in detail in the following points:

  • Local minima: The objective of training a neural network is to find the set of weights that minimizes the loss function. This is a high-dimensional optimization problem, and there are many points (sets of weights) where the loss function has local minima. A suboptimal local minimum is a point where the loss is lower than for the nearby points but higher than the global minimum, which is the overall lowest possible loss. The training process can get stuck in such suboptimal local minima. It’s important to remember that the local minima problem exists even in convex loss functions due to the discrete representation that is a part of digital computation.
  • Vanishing/exploding...