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Duration 21 hours
Course Outline
Introduction to Large Language Models
- An overview of Natural Language Processing (NLP)
- Introduction to the concepts of Large Language Models (LLMs)
- Meta AI's key contributions to LLM development
Exploring the Architecture of Meta AI LLMs
- The Transformer architecture and the role of self-attention mechanisms
- Methodologies for training large-scale models
- Comparative analysis with other prominent LLMs (such as GPT, BERT, and T5)
Establishing the Development Environment
- Installation and configuration of Python and Jupyter Notebook
- Navigating Hugging Face and Meta AI’s model repositories
- Leveraging cloud-based or local GPUs for training workloads
Fine-Tuning and Customizing Meta AI LLMs
- Loading and managing pre-trained models
- Fine-tuning models using domain-specific datasets
- Applying transfer learning techniques
Developing NLP Applications with Meta AI LLMs
- Creating 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 improved inference speed
- Utilizing model compression and quantization techniques
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Identifying and mitigating bias in LLMs
- Promoting transparency and fairness in AI model development
- Exploring future trends and advancements in AI
Requirements
- A foundational grasp of machine learning and deep learning principles.
- Practical experience with Python programming.
- Knowledge of core natural language processing (NLP) concepts.
Target Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
