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Duration 21 hours
Course Outline
Foundations of Large Language Models
- An overview of Natural Language Processing (NLP) fundamentals
- Introduction to the concepts behind Large Language Models (LLMs)
- Meta AI's significant contributions to the advancement of LLMs
Analyzing the Architecture of Meta AI LLMs
- Exploring transformer architecture and self-attention mechanisms
- Examining training methodologies for large-scale neural models
- Benchmarking against other prominent LLMs such as GPT, BERT, and T5
Preparing the Development Environment
- Installation and configuration of Python and Jupyter Notebook
- Interacting with Hugging Face and Meta AI's model repositories
- Leveraging cloud-based or local GPU resources for model training
Fine-Tuning and Customizing Meta AI LLMs
- Procedures for loading pre-trained model weights
- Strategies for fine-tuning on domain-specific datasets
- Application of transfer learning techniques
Developing NLP Applications with Meta AI LLMs
- Engineering chatbots and conversational AI interfaces
- Implementing automated text summarization and paraphrasing
- Conducting sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Enhancing performance for faster inference speeds
- Techniques for model compression and quantization
- Deploying LLMs through APIs and major cloud platforms
Ethical Considerations and Responsible AI
- Identifying and mitigating bias within LLMs
- Promoting transparency and fairness in AI model design
- Exploring future trends and ongoing developments in AI
Requirements
- Foundational knowledge of machine learning and deep learning principles
- Proficiency in Python programming
- A solid working familiarity with core Natural Language Processing (NLP) concepts
Intended Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
