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Duration 21 hours
Course Outline
Foundations of Large Language Models
- An overview of Natural Language Processing (NLP)
- Introduction to Large Language Models (LLMs)
- Meta AI's key contributions to LLM advancement
Exploring Meta AI LLM Architecture
- Transformer architecture and self-attention mechanisms
- Training strategies for large-scale models
- Comparative analysis with other LLMs (GPT, BERT, T5, etc.)
Configuring the Development Environment
- Installation and setup of Python and Jupyter Notebook
- Utilizing Hugging Face and Meta AI’s model repositories
- Leveraging cloud-based or local GPUs for training
Fine-Tuning and Customizing Meta AI LLMs
- Loading pre-trained models
- Fine-tuning on domain-specific datasets
- Application of transfer learning techniques
Developing NLP Applications with Meta AI LLMs
- Creating chatbots and conversational AI systems
- Implementing text summarization and paraphrasing tools
- Conducting sentiment analysis and content moderation
Optimizing and Deploying Large Language Models
- Tuning performance for faster inference
- Applying model compression and quantization methods
- Deploying LLMs via APIs and cloud platforms
Ethical Implications and Responsible AI
- Detecting and mitigating bias in LLMs
- Safeguarding transparency and fairness in AI models
- Emerging trends and future developments in AI
Requirements
- Fundamental knowledge of machine learning and deep learning
- Proficiency in Python programming
- Understanding of natural language processing (NLP) principles
Target Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
