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Mastering NLP from Foundations to LLMs

You're reading from  Mastering NLP from Foundations to LLMs

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Pages 340 pages
Edition 1st Edition
Languages
Authors (2):
Lior Gazit Lior Gazit
Profile icon Lior Gazit
Meysam Ghaffari Meysam Ghaffari
Profile icon Meysam Ghaffari
View More author details

Table of Contents (14) Chapters

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

Basic probability for machine learning

Probability provides information about the likelihood of an event occurring. In this field, there are several key terms that are important to understand:

  • Trial or experiment: An action that results in a certain outcome with a certain likelihood
  • Sample space: This encompasses all potential outcomes of a given experiment
  • Event: This denotes a non-empty portion of the sample space

Therefore, in technical terms, probability is a measure of the likelihood of an event occurring when an experiment is conducted.

In this very simple case, the probability of event A with one outcome is equal to the chance of event A divided by the chance of all possible events. For example, in flipping a fair coin, there are two outcomes with the same chance: heads and tails. The chance of having heads will be 1/(1+1) = ½.

In order to calculate the probability, given an event, A, with n outcomes and a sample space, S, the probability of...

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