Building Large Language Model: A comprehensive guide to creating a Large Language Models
Building Large Language Model: A comprehensive guide to creating a Large Language Models (LLMs) (AI Explorer Series) by Et Tu Code
English | December 25, 2023 | ISBN: N/A | ASIN: B0CQZ1WD5X | 104 pages | EPUB | 6.42 Mb
English | December 25, 2023 | ISBN: N/A | ASIN: B0CQZ1WD5X | 104 pages | EPUB | 6.42 Mb
Unlock the secrets of language model development with this comprehensive guide that takes you from the basics to advanced techniques. Whether you're a beginner or an experienced developer, this book covers every aspect of creating your own large language model.
Highlights:
1. Introduction to Language Model Development
- Understand the role of language models in NLP tasks
- Explore the capabilities of large language models
- Dive into essential NLP concepts: text preprocessing, tokenization, sentiment analysis
- Compare different types of language models and their strengths
- Explore popular frameworks: TensorFlow, PyTorch, Keras
- Make informed decisions on selecting the best framework for your project
- Learn effective data collection and preprocessing techniques
- Master best practices for data augmentation and normalization
- Explore architecture designs: neural networks, transformers, attention mechanisms
- Design an effective model tailored to your project needs
- Step-by-step guide for training and fine-tuning your language model
- Cover hyperparameter tuning, model evaluation, and selection
- Understand metrics: perplexity, accuracy, F1 score
- Implement validation techniques for accurate model performance
- Tips for deploying in applications like chatbots and sentiment analysis tools
- Cover model serving and containerization best practices
- Adapt your model for text classification, question answering, and more
- Guide on dataset preparation, model adaptation, and hyperparameter tuning
- Address ethical concerns: fairness, privacy, transparency
- Mitigate biases in your language model development
- Techniques for performance optimization: quantization, pruning, knowledge distillation
- Emphasize model parallelism and distributed training
- Overview of BERT, RoBERTa, XLNet, and more
- Understand their strengths, weaknesses, and applications
- Tips on integrating with chatbots, voice assistants, and content generation systems
- Best practices for integration frameworks and API design
- Importance of scaling and distributed training for large language models
- Techniques: parallelization, distributed optimization, GPU utilization
- Explore challenges and future trends: domain adaptation, multimodal models, explainability
- Insights into emerging technologies and their impact on the field