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.

• 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