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, the following will be necessary:

  • Programming knowledge: Familiarity with Python programming is a must since the open source models, OpenAI’s API, and LangChain are all illustrated using Python code.
  • Access to OpenAI’s API: An API key from OpenAI will be required to explore closed source models. This can be obtained by creating an account with OpenAI and agreeing to their terms of service.
  • Open source models: Access to the specific open source models mentioned in this chapter will be necessary. These can be accessed and downloaded from their respective repositories or via package managers such as pip or conda.
  • Local development environment: A local development environment setup with Python installed is required. An integrated development environment (IDE) such as PyCharm, Jupyter Notebook, or a simple text editor can be used. Note that we recommend a free Google Colab notebook, as it encapsulates all these requirements...