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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Foundations of Natural Language Processing (NLP).
- Core concepts and introduction to Large Language Models (LLMs).
- An overview of Meta AI’s significant contributions to LLM development.
Understanding the Architecture of Meta AI LLMs
- In-depth look at Transformer architecture and self-attention mechanisms.
- Methodologies used for training large-scale models.
- Comparative analysis with other prominent LLMs (such as GPT, BERT, and T5).
Setting Up the Development Environment
- Installation and configuration of Python and Jupyter Notebook.
- Navigating and utilizing Hugging Face and Meta AI’s model repositories.
- Leveraging cloud-based or local GPU resources for model training.
Fine-Tuning and Customizing Meta AI LLMs
- Procedures for loading pre-trained models.
- Strategies for fine-tuning on domain-specific datasets.
- Application of transfer learning techniques.
Building NLP Applications with Meta AI LLMs
- Development of chatbots and conversational AI interfaces.
- Implementation of automated text summarization and paraphrasing tools.
- Techniques for sentiment analysis and content moderation.
Optimizing and Deploying Large Language Models
- Strategies for enhancing inference speed and performance.
- Application of model compression and quantization methods.
- Deployment of LLMs via APIs and integrated cloud platforms.
Ethical Considerations and Responsible AI
- Methods for detecting and mitigating bias within LLMs.
- Practices to ensure transparency and fairness in AI model outputs.
- Exploration of future trends and advancements in AI technology.
Requirements
- A solid foundational understanding of machine learning and deep learning principles.
- Proficiency in Python programming.
- Working knowledge of core Natural Language Processing (NLP) concepts.
Target Audience
- AI Researchers.
- Data Scientists.
- Machine Learning Engineers.
- Software Developers with an interest in NLP technologies.
