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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Overview of Natural Language Processing (NLP)
- Foundational concepts of Large Language Models (LLMs)
- Meta AI's pivotal role in LLM advancement
Examining the Architecture of Meta AI LLMs
- Transformer architecture and self-attention mechanisms
- Training strategies for large-scale models
- Benchmarking against other LLMs (such as GPT, BERT, and T5)
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
- Loading pre-trained model weights
- Fine-tuning using domain-specific datasets
- Applying transfer learning methodologies
Developing NLP Applications with Meta AI LLMs
- Creating chatbots and conversational AI systems
- Executing text summarization and paraphrasing tasks
- Conducting sentiment analysis and content moderation
Optimizing and Deploying Large Language Models
- Enhancing inference speed and performance
- Applying model compression and quantization techniques
- Deploying LLMs via APIs and cloud infrastructure
Ethical Considerations and Responsible AI
- Identifying and mitigating bias in LLMs
- Guaranteeing transparency and fairness in AI models
- Exploring future trends and innovations in AI
Requirements
- Fundamental knowledge of 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
