Engineering Large Language Models: A Practical Guide from Design to Deployment
Sanford Edwards
This audiobook is narrated by a digital voice.
Discover the world of large language models with this comprehensive guide, designed to take you from the initial design stages to the final deployment. This book provides a practical approach to understanding the complexities and intricacies involved in engineering these powerful AI systems. Whether you are a seasoned professional or a curious beginner, this guide offers valuable insights and hands-on knowledge to help you navigate the challenges and opportunities in this cutting-edge field. The book begins by exploring the foundational concepts and principles that underpin large language models. You will learn about the different architectures, algorithms, and techniques used to create these models, as well as the various tools and frameworks available to support your work. Each chapter builds on the previous one, providing a structured and cohesive learning experience that ensures you gain a deep understanding of the subject matter. As you progress through the book, you will encounter real-world examples and case studies that illustrate the practical applications of large language models. These examples cover a wide range of industries and use cases, from natural language processing and sentiment analysis to machine translation and text generation.
Duration - 7h 12m.
Author - Sanford Edwards.
Narrator - Digital Voice Michelle G.
Published Date - Wednesday, 15 January 2025.
Copyright - © 2025 James Kliewer ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. Discover the world of large language models with this comprehensive guide, designed to take you from the initial design stages to the final deployment. This book provides a practical approach to understanding the complexities and intricacies involved in engineering these powerful AI systems. Whether you are a seasoned professional or a curious beginner, this guide offers valuable insights and hands-on knowledge to help you navigate the challenges and opportunities in this cutting-edge field. The book begins by exploring the foundational concepts and principles that underpin large language models. You will learn about the different architectures, algorithms, and techniques used to create these models, as well as the various tools and frameworks available to support your work. Each chapter builds on the previous one, providing a structured and cohesive learning experience that ensures you gain a deep understanding of the subject matter. As you progress through the book, you will encounter real-world examples and case studies that illustrate the practical applications of large language models. These examples cover a wide range of industries and use cases, from natural language processing and sentiment analysis to machine translation and text generation. Duration - 7h 12m. Author - Sanford Edwards. Narrator - Digital Voice Michelle G. Published Date - Wednesday, 15 January 2025. Copyright - © 2025 James Kliewer ©.
Language:
English
Chapter 1: Introduction to Large Language Models 4
Duración:00:00:05
1.1 What are Large Language Models? 4
Duración:00:07:04
1.2 History and Evolution 9
Duración:00:06:12
1.3 Importance in Modern Technology 13
Duración:00:07:23
1.4 Overview of the Book 18
Duración:00:05:34
Chapter 2: Understanding the Basics of NLP 23
Duración:00:00:05
2.1 Natural Language Processing (NLP) Fundamentals 23
Duración:00:09:07
2.2 Key Concepts and Terminologies 29
Duración:00:10:40
2.3 Text Preprocessing Techniques 37
Duración:00:07:51
2.4 Text Representation Methods 42
Duración:00:05:59
Chapter 3: Fundamentals of Machine Learning for NLP 47
Duración:00:00:05
3.1 Supervised and Unsupervised Learning 47
Duración:00:06:44
3.2 Common Algorithms and Models 52
Duración:00:09:23
3.3 Evaluation Metrics for NLP Models 59
Duración:00:09:30
3.4 Handling Imbalanced Data 65
Duración:00:09:19
Chapter 4: Deep Learning for Large Language Models 73
Duración:00:00:05
4.1 Introduction to Deep Learning 73
Duración:00:07:30
4.2 Neural Networks and Architectures 78
Duración:00:06:14
4.3 Training Deep Learning Models 83
Duración:00:06:07
4.4 Regularization and Optimization Techniques 87
Duración:00:05:53
Chapter 5: Designing Large Language Models 92
Duración:00:00:04
5.1 Architecture Selection 92
Duración:00:07:57
5.2 Model Size and Complexity 98
Duración:00:05:22
5.3 Data Requirements and Collection 101
Duración:00:06:55
5.4 Ethical Considerations in Design 106
Duración:00:08:20
Chapter 6: Data Preparation and Management 113
Duración:00:00:05
6.1 Data Collection Strategies 113
Duración:00:07:54
6.2 Data Cleaning and Preprocessing 118
Duración:00:07:53
6.3 Data Augmentation Techniques 124
Duración:00:07:45
6.4 Managing Large-Scale Datasets 129
Duración:00:05:16
Chapter 7: Training Large Language Models 134
Duración:00:00:04
7.1 Training Process Overview 134
Duración:00:07:37
7.2 Distributed Training Techniques 139
Duración:00:06:11
7.3 Hardware and Software Requirements 144
Duración:00:07:23
7.4 Monitoring and Debugging Training Processes 149
Duración:00:08:34
Chapter 8: Fine-Tuning and Transfer Learning 156
Duración:00:00:04
8.1 Fine-Tuning Pre-Trained Models 156
Duración:00:07:45
8.2 Transfer Learning Techniques 161
Duración:00:06:37
8.3 Domain Adaptation 166
Duración:00:06:26
8.4 Case Studies and Best Practices 171
Duración:00:25:09
Chapter 9: Evaluation and Validation 189
Duración:00:00:05
9.1 Model Evaluation Metrics 189
Duración:00:07:06
9.2 Validation Techniques 194
Duración:00:07:13
9.3 Error Analysis 199
Duración:00:07:15
9.4 Improving Model Performance 204
Duración:00:06:24
Chapter 10: Deployment Strategies 209
Duración:00:00:04
10.1 Deployment Environments 209
Duración:00:06:16
10.2 Model Serving and Scaling 213
Duración:00:07:20
10.3 API Design and Integration 219
Duración:00:07:11
10.4 Monitoring and Maintenance 223
Duración:00:05:04
Chapter 11: Optimization and Performance Tuning 228
Duración:00:00:05
11.1 Model Compression Techniques 228
Duración:00:05:13
11.2 Quantization and Pruning 232
Duración:00:05:26
11.3 Efficient Inference Techniques 236
Duración:00:07:22
11.4 Performance Benchmarking 241
Duración:00:06:31
Chapter 12: Ethical and Societal Implications 246
Duración:00:00:05
12.1 Bias and Fairness in AI 246
Duración:00:07:31
12.2 Privacy Concerns 251
Duración:00:05:40
12.3 Transparency and Explainability 255
Duración:00:08:37
12.4 Regulatory and Compliance Issues 261
Duración:00:07:28
Chapter 13: Case Studies and Applications 267
Duración:00:00:04
13.1 Real-World Applications 267
Duración:00:07:39
13.2 Success Stories and Lessons Learned 272
Duración:00:06:23
13.3 Industry-Specific Use Cases 277
Duración:00:21:15
13.4 Future Trends and Innovations 291
Duración:00:07:19
Chapter 14: Conclusion and Future Directions 297
Duración:00:00:05
14.1 Recap of Key Concepts 297
Duración:00:04:47
14.2 Emerging Trends in NLP and AI 300
Duración:00:07:42
14.3 The Road Ahead for Large Language Models 306
Duración:00:08:29
14.4 Final Thoughts 311
Duración:00:07:01