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Duration 21 hours
Course Outline
Foundations of Large Language Models
- An overview of Natural Language Processing (NLP).
- Introducing the concept and scope of Large Language Models (LLMs).
- Meta AI’s key contributions to the advancement of LLMs.
Analyzing the Architecture of Meta AI LLMs
- Examining Transformer architecture and self-attention mechanisms.
- Strategies for training large-scale models.
- Comparative analysis with other leading LLMs such as GPT, BERT, and T5.
Configuring the Development Workspace
- Setting up and adjusting Python and Jupyter Notebook environments.
- Utilizing Hugging Face and Meta AI’s model repositories.
- Leveraging cloud-based or local GPU resources for model training.
Adapting and Fine-Tuning Meta AI LLMs
- Integrating pre-trained models into your workflow.
- Refining models using domain-specific datasets.
- Applying transfer learning techniques for enhanced performance.
Creating NLP Solutions with Meta AI LLMs
- Engineering chatbots and conversational AI systems.
- Developing text summarization and paraphrasing tools.
- Implementing sentiment analysis and content moderation features.
Enhancing Performance and Deployment
- Optimizing inference speed and overall performance.
- Applying model compression and quantization methods.
- Deploying LLMs through APIs and cloud-based platforms.
Ethical Frameworks and Responsible AI
- Identifying and addressing biases within LLMs.
- Promoting transparency and fairness in AI model development.
- Exploring future trends and advancements in artificial intelligence.
Requirements
- A foundational grasp 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.
