The NLP Book: Mastering NLP from Foundations to LLMs
Book subtitle: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python
by Lior Gazit, and Meysam Ghaffari.
This book is available for order as a hard copy, PDF, or Kindle.
Terminology 🧐:
AI: Artificial intelligence
ML: Machine learning
DL: Deep learning
NLP: Natural language processing
LLM: Large language model
How do we keep up with the recent trends of AI, ML, and NLP?
🤿In the book we dive into the fascinating realm of NLP and unlock this growing field
From understanding the intricacies of text classification to exploring advanced LLM system design, join us as we explain components of NLP.
The necessary skills you would need for applying the solutions:
Math and coding.
In particular, basic undergrad math, as we teach you about the specific Prob&Stat as well as the Linear Algebra.
And as for coding, we provide a vast amount of codes and pipelines, all in Python.
What we the authors bring to the book:
A few years ago, Meysam and I, made a decision to get into NLP. We were already experienced with ML and we felt that NLP has a great potential to generate value. We followed, read, learned, experimented, collaborated, employed, and grew✊️
We developed practical and productionized business solutions and published in journals and conferences.
An overview of the book:
What you will learn
- Master the mathematical foundations of ML and NLP
- Implement advanced techniques for preprocessing text data and analysis design ML-NLP systems in Python
- Model and classify text using traditional ML and DL 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.
Our experts are:
• Xavier Amatriain, VP of Product, Core ML/AI, Google
• Melanie Garson, Cyber Policy & Tech Geopolitics Lead at Tony Blair Institute for Global Change, and Associate Professor at University College London
• Nitzan Mekel-Bobrov, CAIO, Ebay
• David Sontag, Professor at MIT and CEO at Layer Health
• John D. Halamka, president of the Mayo Clinic Platform
Who this book is for
This book is for DL and ML researchers, for NLP practitioners, for educators, and STEM students. Professionals working with text data as part of their projects will find plenty of useful information in this book. Beginner-level familiarity with ML and a basic working knowledge of Python will help you get the best out of this book.
Table of contents by chapters
- Navigating the NLP Landscape: A comprehensive introduction
- Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
- Unleashing Machine Learning Potentials in NLP
- Streamlining Text Preprocessing Techniques for Optimal NLP Performance (Notebooks for chapter 4)
- Empowering Text Classification: Leveraging Traditional Machine Learning Techniques (Notebooks for chapter 5)
- Text Classification Reimagined: Delving Deep into Deep Learning Language Models (Notebooks for chapter 6)
- Demystifying Large Language Models: Theory, Design, and Langchain Implementation
- Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG (Notebooks for chapter 8)
- Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs (Notebooks for chapter 9)
- Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
- Exclusive Industry Insights: Perspectives and Predictions from World Class Experts
Github repo
Our publisher
Proud to have Packt as our publisher for this one.
That’s it, let’s expand your tool bag 🔧
🗣Feel free to drop a question below
The book will be translated to several other languages, let us know whether there’s a particular language you require.
