Mastering NLP from Foundations to LLMs : Apply Advanced Rule-Based Techniques to LLMs and Solve Real-world Business Problems Using Python.

Format:

Book

Author/Creator:

Gazit, Lior.

Contributor:

Ghaffari, Meysam.

Saxena, Asha.

Language:

English

Subjects (All):

ChatGPT.

Artificial intelligence--Data processing.

Artificial intelligence.

Natural language processing (Computer science).

Cloud computing.

Physical Description:

1 online resource (340 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2024.

Biography/History:

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.

Summary:

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.

Contents:

Cover

Title page

Copyright and credits

Dedication

Foreword

Contributors

Disclaimer

Table of Contents

Preface

Chapter 1: Navigating the NLP Landscape: A comprehensive introduction

Who this book is for

What is natural language processing?

The history and evolution of natural language processing

Initial strategies in the machine processing of natural language

A winning synergy - the coming together of NLP and ML

Introduction to math and statistics in NLP

Understanding language models - ChatGPT example

Summary

Questions and answers

Chapter 2: Linear Algebra, Probability and Statistics, and Estimation for Machine Learning and Natur

Introduction to linear algebra

Basic operations on matrices and vectors

Matrix definitions

Eigenvalues and eigenvectors

Numerical methods for finding eigenvectors

Eigenvalue decomposition

Singular value decomposition

Basic probability for machine learning

Statistically independent

Discrete random variables and their distribution

Probability density function

Bayesian estimation

Further reading

References

Chapter 3: Machine Learning for Natural Language Processing

Technical requirements

Data exploration

Data visualization

Data cleaning

Feature selection

Feature engineering

Common machine learning models

Linear regression

Logistic regression

Decision trees

Random forest

Support vector machines (SVMs)

Neural networks and transformers

Model underfitting and overfitting

Splitting data

Hyperparameter tuning

Ensemble models

Bagging

Boosting

Stacking

Random forests

Gradient boosting

Handling imbalanced data

SMOTE

The NearMiss algorithm

Cost-sensitive learning

Data augmentation

Dealing with correlated data

References.

Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance

Lowercasing in NLP

Removing special characters and punctuation

Stop word removal

NER

POS tagging

Rule-based methods

Statistical methods

Deep learning-based methods

Regular expressions

Tokenization

Explaining the preprocessing pipeline

Code for NER and POS

Chapter 5: Text Classification, Part 1 - Using Traditional Machine Learning

Types of text classification

Supervised learning

Unsupervised learning

Semi-supervised learning

Sentence classification using one-hot encoding vector representation

Text classification using TF-IDF

Text classification using Word2Vec

Word2Vec

Model evaluation

Overfitting and underfitting

Additional topics in applied text classification

Topic modeling - a particular use case of unsupervised text classification

LDA

Real-world ML system design for NLP text classification

Implementing an ML solution

Reviewing our use case - ML system design for NLP classification in a Jupyter Notebook

The pipeline

Code settings

Generating the chosen model

Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models

Understanding deep learning basics

What is a neural network?

The basic design of a neural network

Neural network common terms

The architecture of different neural networks

The challenges of training neural networks

Language models

Transfer learning

Understanding transformers

Architecture of transformers

Applications of transformers

Learning more about large language models.

The challenges of training language models

Specific designs of language models

Challenges of using GPT-3

Reviewing our use case - ML/DL system design for NLP classification in a Jupyter Notebook

The business objective

The technical objective

Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation

What are LLMs and how are they different from LMs?

n-gram models

Hidden Markov models (HMMs)

Recurrent neural networks (RNNs)

How LLMs stand out

Motivations for developing and using LLMs

Improved performance

Broad generalization

Few-shot learning

Understanding complex contexts

Multilingual capabilities

Human-like text generation

Challenges in developing LLMs

Amounts of data

Computational resources

Risk of bias

Model robustness

Interpretability and debugging

Environmental impact

Different types of LLMs

Transformer models

Example designs of state-of-the-art LLMs

GPT-3.5 and ChatGPT

LM pretraining

Training the reward model

How to fine-tune the model using reinforcement learning

GPT-4

LLaMA

PaLM

Open-source tools for RLHF

Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG

Setting up an LLM application - API-based closed source models

Choosing a remote LLM provider

Prompt engineering and priming GPT

Experimenting with OpenAI's GPT model

Setting up an LLM application - local open source models

About the different aspects that distinguish between open source and closed source

Hugging Face's hub of models

Employing LLMs from Hugging Face via Python

Exploring advanced system design - RAG and LangChain

LangChain's design concepts

Data sources.

Data that is not pre-embedded

Chains

Agents

Long-term memory and referring to prior conversations

Ensuring continuous relevance through incremental updates and automated monitoring

Reviewing a simple LangChain setup in a Jupyter notebook

Setting up a LangChain pipeline with Python

LLMs in the cloud

AWS

Microsoft Azure

GCP

Concluding cloud services

Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs

Enhancing LLM performance with RAG and LangChain - a dive into advanced functionalities

LangChain pipeline with Python - enhancing performance with LLMs

Advanced methods with chains

Asking the LLM a general knowledge question

Requesting output structure - making the LLM provide output in a particular data format

Evolving to a fluent conversation - inserting an element of memory to have previous interactions as reference and context for follow-up prompts

Retrieving information from various web sources automatically

Retrieving content from a YouTube video and summarizing it

Prompt compression and API cost reduction

Prompt compression

Experimenting with prompt compression and evaluating trade-offs

Multiple agents - forming a team of LLMs that collaborate

Potential advantages of multiple LLM agents working simultaneously

Concluding thoughts on the multiple-agent team

Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI

Key technical trends around LLMs and AI

Computation power - the engine behind LLMs

The future of computational power in NLP

Large datasets and their indelible mark on NLP and LLMs

Purpose - training, benchmarking, and domain expertise

Value - robustness, diversity, and efficiency.

Impact - democratization, proficiency, and new concerns

Evolution of large language models - purpose, value, and impact

Purpose - why the push for bigger and better LLMs?

Value - the LLM advantage

Impact - changing the landscape

NLP and LLMs in the business world

Business sectors

Customer interactions and service - the early adopter

Change management driven by AI's impact

Behavioral trends induced by AI and LLMs - the social aspect

Personal assistants becoming indispensable

Ease in communication and bridging language barriers

Ethical implications of delegated decisions

Ethics and risks - growing concerns around the implementation of AI

Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

Overview of our experts

Nitzan Mekel-Bobrov, PhD

David Sontag, PhD

John D. Halamka, M.D., M.S.

Xavier Amatriain, PhD

Melanie Garson, PhD

Our questions and the experts' answers

Nitzan Mekel-Bobrov

Q1.1 - Future of LLM - hybrid learning paradigms: In light of the evolving landscape of learning schemes, what do you envision as the next breakthrough in combining different learning paradigms within LLMs?

Q2.1 - As the Chief AI Officer becomes more integral to the corporate hierarchy, what unique challenges do you foresee in bridging the gap between AI potential and practical business applications, and how should the CAIO's role evolve to meet these challe

Q3 - How do foundation models and the strategies of major tech companies toward open sourcing affect data ownership and its value for businesses?

David Sontag

Q1 - As we progress toward creating more equitable and unbiased datasets, what strategies do you believe are most effective in identifying and mitigating implicit biases within large datasets?.

Q2 - How do you see these strategies evolving with the advancement of NLP technologies, and what do you envision as the next breakthrough in combining different learning paradigms within LLMs?.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781804616383

1804616389

OCLC:

1430322280