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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Foundations of Natural Language Processing (NLP)
- Overview of Large Language Models (LLMs)
- Meta AI’s role in advancing LLM development
Deconstructing the Architecture of Meta AI LLMs
- Exploration of Transformer architecture and self-attention mechanisms
- Strategies for training large-scale models
- Benchmarking against other LLMs such as GPT, BERT, and T5
Configuring the Development Environment
- Installation and setup of Python and Jupyter Notebook
- Integrating with Hugging Face and Meta AI’s model repository
- Leveraging cloud-based or local GPUs for training workloads
Fine-Tuning and Customizing Meta AI LLMs
- Methodologies for loading pre-trained models
- Adapting models through fine-tuning on domain-specific datasets
- Applying transfer learning techniques
Developing NLP Applications with Meta AI LLMs
- Creating chatbots and conversational AI interfaces
- Building systems for text summarization and paraphrasing
- Implementing sentiment analysis and content moderation features
Optimizing and Deploying Large Language Models
- Enhancing inference speed through performance tuning
- Applying model compression and quantization methods
- Distributing LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Identifying and mitigating bias within LLMs
- Maintaining transparency and fairness in AI model behavior
- Tracking future trends and advancements in the AI landscape
Requirements
- A solid foundation in machine learning and deep learning principles
- Proficiency in Python programming
- Working knowledge of Natural Language Processing (NLP) concepts
Target Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
