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Duration 21 hours
Course Outline
Introduction to Large Language Models
- An overview of Natural Language Processing (NLP)
- Fundamentals of Large Language Models (LLMs)
- Meta AI's key contributions to LLM development
Exploring the Architecture of Meta AI LLMs
- Transformer architectures and self-attention mechanisms
- Training methodologies for large-scale models
- Comparative analysis with other LLMs (e.g., GPT, BERT, T5)
Configuring 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 on domain-specific datasets
- Applying transfer learning techniques
Building NLP Applications with Meta AI LLMs
- Creating chatbots and conversational AI systems
- Implementing text summarization and paraphrasing features
- Conducting sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Tuning performance for faster inference
- Applying model compression and quantization techniques
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Detecting and mitigating bias in LLMs
- Ensuring transparency and fairness in AI models
- Exploring future trends and developments in AI
Requirements
- A foundational understanding of machine learning and deep learning principles
- Proficiency in 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
