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Duration 21 hours
Course Outline
Foundations of Large Language Models
- Core concepts in Natural Language Processing (NLP).
- An introduction to the landscape of Large Language Models (LLMs).
- Meta AI’s pivotal role in advancing LLM technology.
Architecture of Meta AI LLMs
- Examining Transformer architectures and self-attention mechanisms.
- Strategies for training large-scale models effectively.
- Benchmarking against other prominent LLMs, such as GPT, BERT, and T5.
Development Environment Configuration
- Installation and configuration of Python and Jupyter Notebook environments.
- Integrating with Hugging Face and Meta AI’s model repositories.
- Leveraging cloud-based or local GPU resources for training tasks.
Customization via Fine-Tuning
- Methods for loading pre-trained model weights.
- Techniques for fine-tuning on domain-specific datasets.
- Application of transfer learning strategies.
Developing NLP Solutions
- Engineering conversational AI systems and chatbots.
- Implementing robust text summarization and paraphrasing capabilities.
- Conducting sentiment analysis and content moderation.
Optimization and Deployment Strategies
- Enhancing inference speed through performance tuning.
- Utilizing model compression and quantization techniques.
- Deploying LLMs via APIs and cloud platform services.
Ethics and Responsible AI Practice
- Identifying and mitigating bias within LLMs.
- Safeguarding transparency and fairness in AI model outputs.
- Exploring emerging trends and future developments in AI.
Requirements
- A solid foundation in 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 technologies.
