LLM Training: Techniques and Applications is a comprehensive guide to training large language models, detailing the processes, tools, and best practices involved. This book is ideal for developers, data scientists, and AI researchers seeking to deepen their understanding of LLM training workflows. It covers essential topics, including data preparation, model architecture design, distributed training techniques, and hyperparameter tuning. The book delves into both supervised and unsupervised learning approaches, offering detailed explanations of how to handle large datasets, manage computational resources, and address common training challenges like overfitting and underfitting. It also provides hands-on exercises with popular frameworks like TensorFlow and PyTorch. Additionally, LLM Training explores cutting-edge techniques for fine-tuning models for specific tasks and domains, leveraging transfer learning, and improving model performance through continual learning. The book also includes case studies that showcase successful LLM training across industries such as healthcare, finance, and natural language processing. By the end of this book, readers will have a deep understanding of the intricacies of training LLMs and will be equipped with the skills needed to optimize models for various real-world applications
