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)

Hyperparameter tuning

Hyperparameter tuning is an important step in the machine learning process that involves selecting the best set of hyperparameters for a given model. Hyperparameters are values that are set before the training process begins and can have a significant impact on the model’s performance. Examples of hyperparameters include learning rate, regularization strength, number of hidden layers in a neural network, and many others.

The process of hyperparameter tuning involves selecting the best combination of hyperparameters that results in the optimal performance of the model. This is typically done by searching through a predefined set of hyperparameters and evaluating their performance on a validation set.

There are several methods for hyperparameter tuning, including grid search, random search, and Bayesian optimization. Grid search involves creating a grid of all possible hyperparameter combinations and evaluating each one on a validation set to determine...