Designing and Training Large Language Models
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Designing and Training Large Language Models
About the Book
About the Author
Table of Contents
- Editor’s Note
- The Ioka Project
- Copyrights
- Notes on Current Edition
- Preface
- Introduction to Large Language Models: Unraveling the Complexity and Mastery of AI’s Linguistic Architects
- Introduction to the relevance of large language models.
- Overview of the book’s goals and structure.
- Target audience and required prior knowledge.
- The treatment of this section aims to ensure that readers from diverse backgrounds feel adequately guided and supported as they embark on the enriching journey that the book promises.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 1: Introduction to Large Language Models
- Chapter Introduction: The Evolution and Essence of Large Language Models
- Definition of language models.
- Understanding the importance of scale.
- Brief overview of applications.
- The evolution from rule-based to statistical, to neural network-based language models.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 2: A Brief History of Language Modeling
- Introduction to The Transformation of Computational Linguistics and Language Modeling
- Early computational linguistic efforts.
- Emergence of statistical methods.
- The deep learning revolution.
- Milestones: From RNNs and LSTMs to the Transformer architecture.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 3: Foundations of Neural Networks and Deep Learning
- Introduction to Foundations of Neural Networks, Deep Learning, and Language Models
- Introduction to artificial neural networks.
- Backpropagation and gradient descent.
- Deep Dive Deep Learning
- Deep Dive Transformer
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 4: The Transformer Revolution
- Introduction to Transformer Models and Large Language Models in NLP
- Deep dive into the Transformer architecture.
- title: “In-Depth Examination of the Transformer Model”
- Self-attention and positional encoding.
- BERT, GPT, and their successors.
- Comparisons of the Transformer with prior models.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 5: Understanding Large Language Models
- Introduction to Large Language Models: Structure, Training, and Assessment
- Defining ‘large’: parameters, layers, and model size.
- Data requirements for training.
- The role of transfer learning and fine-tuning.
- Evaluation metrics for language models.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 6: Tools and Frameworks
- Introduction to Programming Languages, Libraries, Frameworks, and Hardware for Large Language Models
- Overview of programming languages: Python, C++, others.
- Libraries and frameworks: TensorFlow, PyTorch, Hugging Face Transformers.
- Hardware considerations: GPUs, TPUs.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 7: Data Collection and Processing
- Introduction to Dataset and Preprocessing Essentials for Large Language Models
- Principles of dataset creation.
- Text cleaning and tokenization.
- Handling bias and ethics in training data.
- Techniques for efficient data storage and retrieval.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 8: Model Training and Optimization
- Chapter Introduction: Fine-Tuning the Giants - Training and Optimization of Large Language Models
- Initialization and configuration of large models.
- Optimization algorithms: SGD, Adam, LAMB.
- Techniques for faster convergence: learning rate schedules, warm-up.
- Handling overfitting: dropout, regularization.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 9: Distributed and Parallel Training
- Introduction to the Chapter on Distributed and Parallel Training for Large Language Models
- Introduction to distributed training.
- Data parallelism vs. model parallelism.
- Efficient utilization of hardware resources.
- Challenges and solutions for synchronous and asynchronous updates.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 10: Fine-tuning and Transfer Learning
- Chapter Introduction: Optimizing Linguistic Versatility in Large Language Models through Transfer Learning and Fine-Tuning
- Principles of transfer learning.
- Techniques and strategies for fine-tuning models.
- Case studies and examples.
- The balance between generalization and specialization.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 11: Case Studies of Known Large Language Models
- Introduction to Large Language Models - Analysis, Case Studies, and Domain-Specific Adaptations
- Complete list of known large models: GPT-3, BERT, RoBERTa, T5, etc.
- Breakdown of similarities and differences.
- Analysis of use cases and domain-specific adaptations.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 12: Ethical Considerations and Societal Impact
- Introduction to Ethical and Societal Aspects of AI
- Bias and fairness in language models.
- Misuse potential: deep fakes, misinformation.
- Regulatory and ethical frameworks.
- Sustainable AI and environmental considerations.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 13: The Future of Language Models
- Introduction to Language Model Predictions and Architectures
- Predictions for the future.
- Next-generation model architectures.
- Integration of multimodal data.
- Broader applications and ethical implications.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Chapter 14: Applied Best Practices
- Introduction to Designing, Deploying, and Managing Large Language Models
- Guidelines for designing your own language model.
- Case studies on effective training and deployment.
- Tips on monitoring, maintaining, and updating models.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Appendices
- Introduction to Language Modeling Essentials and Resources
- Glossary of key terms in language modeling.
- Additional resources: articles, websites, courses.
- List of open-source tools and datasets.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- References
- Chapter Introduction: “Understanding Large Language Models: A Comprehensive Guide”
- Chapter Introduction: “Navigating AI Resources: A Guide to Online Materials and Documentation”
- Academic papers.
- Online resources and official documentation for tools and frameworks.
- Relevant books and articles.
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Index
- Introduction to Large Language Models
- Case Study (Fictional)
- Chapter Summary
- Further Reading
- Editor’s Note
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