Gazit / Ghaffari Mastering NLP from Foundations to LLMs

Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python
1. Auflage 2024
ISBN: 978-1-80461-638-3
Verlag: De Gruyter
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 340 Seiten

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.

Autoren/Hrsg.

Gazit, Lior

Ghaffari, Meysam

Weitere Mitwirkende

Saxena, Asha

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - 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
- Empowering Text Classification: Leveraging Traditional Machine Learning Techniques
- Text Classification Reimagined: Delving Deep into Deep Learning Language Models
- Demystifying Large Language Models: Theory, Design, and Langchain Implementation
- Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG
- Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs
- Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
- Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

Über Autor(innen)

Gazit Lior :

Lior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations.Ghaffari Meysam  :

Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.