Mastering NLP from Foundations to LLMs
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內容簡介
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 FeaturesLearn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPTMaster embedding techniques and machine learning principles for real-world applicationsUnderstand the mathematical foundations of NLP and deep learning designsPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnMaster the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in PythonModel and classify text using traditional machine learning and deep learning methodsUnderstand the theory and design of LLMs and their implementation for various applications in AIExplore NLP insights, trends, and expert opinions on its future direction and potentialWho 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 ContentsNavigating the NLP Landscape: A comprehensive introductionMastering Linear Algebra, Probability, and Statistics for Machine Learning and NLPUnleashing Machine Learning Potentials in NLPStreamlining Text Preprocessing Techniques for Optimal NLP PerformanceEmpowering Text Classification: Leveraging Traditional Machine Learning TechniquesText Classification Reimagined: Delving Deep into Deep Learning Language ModelsDemystifying Large Language Models: Theory, Design, and Langchain ImplementationAccessing the Power of Large Language Models: Advanced Setup and Integration with RAGExploring the Frontiers: Advanced Applications and Innovations Driven by LLMsRiding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AIExclusive Industry Insights: Perspectives and Predictions from World Class Experts
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