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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 key contributions to the advancement of LLMs
Exploring the Architecture of Meta AI LLMs
- Detailed examination of Transformer architectures and self-attention mechanisms
- Methodologies for training large-scale models
- Comparative analysis with other prominent LLMs (GPT, BERT, T5, etc.)
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 model training
Fine-Tuning and Customizing Meta AI LLMs
- Techniques for loading pre-trained models
- Fine-tuning processes on domain-specific datasets
- Application of transfer learning strategies
Developing NLP Applications with Meta AI LLMs
- Construction of chatbots and conversational AI systems
- Implementation of text summarization and paraphrasing features
- Application of sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Performance tuning to enhance inference speed
- Techniques for model compression and quantization
- Deployment strategies using APIs and cloud platforms
Ethical Considerations and Responsible AI
- Identification and mitigation of bias in LLMs
- Safeguarding transparency and fairness in AI models
- Insights into 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
