Mastering NLP from Foundations to LLMs

Authors:

• Lior Gazit.
• Meysam Ghaffari.

This notebook is taught and reviewed in our book:
Mastering NLP from Foundations to LLMs

Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends

Key Features

• Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT
• Master embedding techniques and machine learning principles for real-world applications
• Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python
• Model and classify text using traditional machine learning and deep learning methods
• Understand the theory and design of LLMs and their implementation for various applications in AI
• Explore NLP insights, trends, and expert opinions on its future direction and potential

Who this book is for

This book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.

Table of Contents

1. Navigating the NLP Landscape: A comprehensive introduction
2. Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
3. Unleashing Machine Learning Potentials in NLP
4. Streamlining Text Preprocessing Techniques for Optimal NLP Performance (Notebooks for chapter 4)
5. Empowering Text Classification: Leveraging Traditional Machine Learning Techniques (Notebooks for chapter 5)
6. Text Classification Reimagined: Delving Deep into Deep Learning Language Models (Notebooks for chapter 6)
7. Demystifying Large Language Models: Theory, Design, and Langchain Implementation
8. Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG (Notebooks for chapter 8)
9. Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs (Notebooks for chapter 9)
10. Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
11. Exclusive Industry Insights: Perspectives and Predictions from World Class Experts