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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Fundamentals of Natural Language Processing (NLP).
- An overview of Large Language Models (LLMs).
- Meta AI's key contributions to the evolution of LLMs.
Exploring Meta AI LLM Architecture
- In-depth look at Transformer architecture and self-attention mechanisms.
- Methodologies for training large-scale models.
- Comparative analysis with other prominent LLMs (e.g., GPT, BERT, T5).
Configuring the Development Environment
- Setting up Python and Jupyter Notebook for LLM development.
- Navigating Hugging Face and Meta AI’s model repositories.
- Leveraging cloud-based or local GPU resources for training tasks.
Fine-Tuning and Customizing Meta AI LLMs
- Techniques for loading pre-trained models.
- Strategies for fine-tuning on domain-specific datasets.
- Application of transfer learning techniques.
Developing NLP Applications with Meta AI LLMs
- Creating intelligent chatbots and conversational AI agents.
- Implementing solutions for text summarization and paraphrasing.
- Performing sentiment analysis and content moderation.
Optimization and Deployment of Large Language Models
- Tuning performance for faster inference speeds.
- Applying model compression and quantization methods.
- Deploying LLMs via APIs and cloud platforms.
Ethical Considerations and Responsible AI
- Identifying and mitigating bias in LLMs.
- Promoting transparency and fairness in AI model design.
- Exploring future trends and emerging developments in AI.
Requirements
- A foundational understanding of machine learning and deep learning principles.
- Proficiency in Python programming.
- Familiarity with core concepts in Natural Language Processing (NLP).
Target Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
