Mastering Large Language Models  
Advanced techniques, applications, cutting-edge methods, and top LLMs (English Edition)
Published by BPB Publications
Publication Date:  Available in all formats
ISBN: 9789355519658
Pages: 380

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ISBN: 9789355519658 Price: INR 899.00
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Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks, and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices.
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Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks, and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices.
Table of contents
  • Cover
  • Title Page
  • Copyright Page
  • Dedication Page
  • About the Author
  • About the Reviewers
  • Acknowledgement
  • Preface
  • Table of Contents
  • 1. Fundamentals of Natural Language Processing
    • Introduction
    • Structure
    • Objectives
    • The definition and applications of NLP
      • What exactly is NLP
      • Why do we need NLP
    • The history and evolution of NLP
    • The components of NLP
      • Speech recognition
      • Natural language understanding
      • Natural language generation
    • Linguistic fundamentals for NLP
      • Morphology
      • Syntax
      • Semantics
      • Pragmatics
    • The challenges of NLP
    • Role of data in NLP applications
    • Conclusion
  • 2. Introduction to Language Models
    • Introduction
    • Structure
    • Objectives
    • Introduction and importance of language models
    • A brief history of language models and their evolution
      • Significant milestones in modern history
      • Transformers: Attention is all you need
      • History of language model post transformers
    • Types of language models
    • Autoregressive and autoencoding language models
      • Autoregressive language models
      • Autoencoding language models
    • Examples of large language models
      • GPT-4
      • PaLM: Google’s Pathways Language Model
    • Training basic language models
      • Training rule-based models
      • Training statistical models
    • Conclusion
  • 3. Data Collection and Pre-processing for Language Modeling
    • Introduction
    • Structure
    • Objectives
    • Data acquisition strategies
      • The power of data collection
      • Language modeling data sources
      • Data collection techniques
      • Open-source data sources
    • Data cleaning techniques
      • Advanced data cleaning techniques for textual data
    • Text pre-processing: preparing text for analysis
    • Data annotation
      • Exploratory data analysis
    • Managing noisy and unstructured data
    • Data privacy and security
    • Conclusion
  • 4. Neural Networks in Language Modeling
    • Introduction
    • Structure
    • Objectives
    • Introduction to neural networks
      • What is a neural network
      • How do neural networks work
      • Feedforward neural networks
        • How feedforward neural networks work
      • What is the activation function
      • Forward propagation process in feedforward neural networks
      • Implementation of feedforward neural network
    • Backpropagation
      • Backpropagation algorithm
    • Gradient descent
      • What is gradient descent
      • Gradient descent in neural network optimization
      • Challenges and considerations
      • Relation between backpropagation and gradient descent
    • Conclusion
  • 5. Neural Network Architectures for Language Modeling
    • Introduction
    • Structure
    • Objectives
    • Understanding shallow and deep neural networks
      • What are shallow neural networks
      • What are deep neural networks
    • Fundamentals of RNN
      • What are RNNs
      • How RNN works
      • Backpropagation through time
      • Vanishing gradient problem
    • Types of RNNs
      • Introduction to LSTMs
        • LSTM architecture
        • Training an LSTM
        • LSTM challenges and limitations
      • Introduction to GRUs
        • GRU architecture
      • Introduction to bidirectional RNNs
        • Key differences summary
    • Fundamentals of CNNs
      • CNN architecture
    • Building CNN-based language models
      • Applications of RNNs and CNNs
    • Conclusion
  • 6. Transformer-based Models for Language Modeling
    • Introduction
    • Structure
    • Objectives
    • Introduction to transformers
    • Key concepts
      • Self-attention
      • Multi-headed attention
      • Feedforward neural networks
      • Positional encoding
    • Transformer architecture
      • High-level architecture
        • Components of encoder and decoder
      • Complete architecture
        • Input and output layer
    • Advantages and limitations of transformers
    • Conclusion
  • 7. Training Large Language Models
    • Introduction
    • Structure
    • Objectives
    • Building a tiny language model
      • Introduction to Tiny LLM
      • How the Tiny LLM works
      • Building a character-level text generation model
        • Core concepts
      • Improving model with word tokenization
        • Core concepts
      • Training on a larger dataset
      • Building using transformers and transfer learning
      • Building effective LLMs
        • Strategies for data collection
        • Model selection
        • Model training
        • Model evaluation
        • Transfer learning
        • Fine-tuning for specific tasks
      • Learning from failures
    • Conclusion
  • 8. Advanced Techniques for Language Modeling
    • Introduction
    • Structure
    • Objectives
    • Meta-learning
      • Why do we need meta-learning?
      • Meta-learning approaches
      • Various meta-learning techniques
      • Advantages of meta-learning
      • Applications of Meta-learning in language modeling
    • Few-shot learning
      • Few-shot learning approaches
      • Metric learning for few-shot learning
      • Practical applications
    • Multi-modal language modeling
      • Types of multi-modal models
      • Data collection and pre-processing for multi-modal models
      • Training and evaluation of multi-modal models
        • Training multi-modal models
        • Evaluation of multi-modal models
      • Applications of multi-modal language modeling
      • Examples of multi-modal language modeling
    • Mixture-of-Expert systems
      • Benefits of using MoE systems
      • Types of Experts in an MoE system
    • Adaptive attention span
      • The challenge of fixed attention
      • Adaptive attention span architecture
      • Advantages of adaptive attention span
      • Applications of adaptive attention span
        • Challenges and ongoing research
    • Vector database
      • Efficient vector representation
      • Building a vector database
        • Advantages of vector database
    • Masked language modeling
      • Concept of masked language modeling
      • Importance of bidirectional context
      • Pretraining and fine-tuning
      • Applications of masked language modeling
      • Challenges and improvements
    • Self-supervised learning
      • The concept of self-supervised learning
      • Leveraging unannotated data
      • Transfer learning and fine-tuning
      • Applications of self-supervised learning
        • Challenges and future developments
    • Reinforcement learning
      • The basics of reinforcement learning
    • Generative adversarial networks
      • The GAN architecture
      • Adversarial training
      • Text generation and understanding
      • Challenges and improvements
    • Conclusion
  • 9. Top Large Language Models
    • Introduction
    • Structure
    • Objectives
    • Top large language models
      • BERT
        • Architecture and training
        • Key features and contributions
      • RoBERTa
        • Architecture and training
        • Key features and contributions
      • GPT-3
        • Key features and contributions
      • Falcon LLM
        • Key features
        • Impact and applications
      • Chinchilla
        • Key features and contributions
      • MT-LNG
        • Architecture and training
        • Key features and contributions
        • Impact and applications
      • Codex
        • Architecture and training
        • Key features and contributions
        • Impact and applications
      • Gopher
        • Architecture and training
        • Key features and contributions
        • Impact and applications
      • GLaM
        • Architecture and training
        • Key features and contributions
        • Impact and applications
      • GPT 4
        • Key features and contributions
        • Impact and applications
      • LLaMa 2
        • Architecture and training
        • Key features and contributions
        • Impact and applications
      • PaLM 2
        • Architecture and training
        • Key features and contributions
        • Impact and applications
    • Quick summary
    • Conclusion
  • 10. Building First LLM App
    • Introduction
    • Structure
    • Objectives
    • The costly endeavor of large language models
      • The costly construction of large language models
      • Leveraging existing models for custom applications
    • Techniques to build custom LLMs apps
    • Introduction to LangChain
      • Solving complexities and enabling accessibility
      • Diverse use cases
      • Key capabilities of LangChain
    • LangChain agent
    • Creating the first LLM app
      • Fine-tuning an OpenAI model
    • Deploying LLM app
    • Conclusion
  • 11. Applications of LLMs
    • Introduction
    • Structure
    • Objectives
    • Conversational AI
      • Introduction to conversational AI
      • Limitations of traditional chatbots
      • Natural language understanding and generation
        • Natural language understanding
        • Natural language generation
      • Chatbots and virtual assistants
        • Chatbots
        • Virtual assistants
      • LLMs for advanced conversational AI
      • Challenges in building conversational agents
      • Successful examples
    • Text generation and summarization
      • Text generation techniques
      • Summarization techniques
      • Evaluation metrics
      • Successful examples
    • Language translation and multilingual models
      • Machine translation techniques
        • RBMT
        • Neural machine translation
      • Multilingual models and cross-lingual tasks
      • Successful examples
    • Sentiment analysis and opinion mining
      • Sentiment analysis techniques
      • Opinion mining
      • Challenges of analyzing subjective language
      • Applications in customer feedback analysis
      • Successful examples
    • Knowledge graphs and question answering
      • Introduction to knowledge graphs
      • Structured information representation and querying
      • Question answering techniques
      • Challenges in building KGs and QA systems
      • Successful examples
    • Retrieval augmented generation
      • Introduction to retrieval-augmented generation
      • Key components of RAG
      • RAG process
      • Advantages of RAG
      • Successful examples
    • Conclusion
  • 12. Ethical Considerations
    • Introduction
    • Structure
    • Objectives
    • Pillars of an ethical framework
    • Bias
      • Impacts
      • Solutions
    • Privacy
      • Impacts
      • Solutions
    • Accountability
      • Impacts
      • Solutions
    • Transparency
      • Impacts
      • Solutions
    • Misuse of language models
      • Impacts
      • Solutions
    • Responsible development
      • Impacts
      • Solutions
    • User control
      • Impacts
      • Solutions
    • Environmental impact
      • Impacts
      • Solutions
    • Conclusion
  • 13. Prompt Engineering
    • Introduction
    • Structure
    • Objectives
    • Understanding prompts
      • What are prompts
      • Why are prompts essential
      • What is prompt engineering
      • Elements of a prompt
    • Role of prompts in NLP tasks
    • Types of prompt engineering
      • Direct prompting
      • Prompting with examples
      • Chain-of-Thought prompting
    • Structuring effective prompts
      • Clarity and precision
      • Context establishment
      • Formatting and structure
      • Specifying constraints
      • Providing examples
    • Designing prompts for different tasks
      • Text summarization
      • Question answering
      • Text classification
      • Role playing
      • Code generation
      • Reasoning
    • Advanced techniques for prompt engineering
      • Knowledge prompting for commonsense reasoning
        • How it works
      • Choosing the right prompt format and structure
      • Selecting the most appropriate keywords and phrases
      • Fine-tuning prompts for specific tasks and applications
      • Evaluating the quality and effectiveness of prompts
    • Key concerns
      • Prompt injection
      • Prompt leaking
      • Jailbreaking
      • Bias amplification
    • Conclusion
  • 14. Future of LLMs and Its Impact
    • Introduction
    • Structure
    • Objectives
    • Future directions for language models
      • Self-improving models
      • Sparse expertise
      • Program-aided language model
      • ReAct: Synergizing reasoning and acting in language models
    • Large language models and impacts on jobs
      • Automation and task redefinition
      • Assistance and augmentation
      • Evolving skill requirements
      • New job creation
    • Impact of language models on society at large
      • Ethical considerations and responsible AI
      • Regulatory landscape
      • Human-AI collaboration
      • Collaborative AI for social good
    • Conclusion
  • Index
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