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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Overview of Natural Language Processing (NLP)
- Foundations of Large Language Models (LLMs)
- Meta AI’s key contributions to the evolution of LLMs
Exploring the Architecture of Meta AI LLMs
- Transformer architecture and the role of self-attention mechanisms
- Methodologies for training large-scale models
- Comparative analysis with other prominent LLMs (GPT, BERT, T5, etc.)
Establishing the Development Environment
- Installation and configuration of Python and Jupyter Notebook
- Utilizing Hugging Face and Meta AI’s model repositories
- Leveraging cloud-based or local GPUs for training tasks
Fine-Tuning and Customizing Meta AI LLMs
- Procedures for loading pre-trained models
- Applying fine-tuning to domain-specific datasets
- Techniques for effective transfer learning
Developing NLP Applications with Meta AI LLMs
- Building chatbots and conversational AI systems
- Implementing text summarization and paraphrasing features
- Conducting sentiment analysis and content moderation
Optimizing and Deploying Large Language Models
- Tuning performance for faster inference speeds
- Applying model compression and quantization techniques
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Identifying and mitigating bias within LLMs
- Ensuring transparency and fairness in AI model operations
- Exploring future trends and advancements in AI
Requirements
- A foundational grasp of machine learning and deep learning principles.
- Practical experience with Python programming.
- Familiarity with core Natural Language Processing (NLP) concepts.
Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
