Large Language Models Meta AI Training Course
Large Language Models (LLMs) developed by Meta AI are powerful deep learning models capable of understanding and generating human-like text. These models are widely used for applications such as chatbots, text summarization, sentiment analysis, and content generation.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI researchers, data scientists, and developers who wish to understand, fine-tune, and implement Meta AI's Large Language Models for various NLP applications.
By the end of this training, participants will be able to:
- Understand the architecture and functioning of Meta AI's Large Language Models.
- Set up and fine-tune Meta AI LLMs for specific use cases.
- Implement LLM-based applications such as text summarization, chatbots, and sentiment analysis.
- Optimize and deploy large language models efficiently.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
Introduction to Large Language Models
- Overview of Natural Language Processing (NLP)
- Introduction to Large Language Models (LLMs)
- Meta AI's contributions to LLM development
Understanding the Architecture of Meta AI LLMs
- Transformer architecture and self-attention mechanisms
- Training methodologies for large-scale models
- Comparison with other LLMs (GPT, BERT, T5, etc)
Setting Up the Development Environment
- Installing and configuring Python and Jupyter Notebook
- Working with Hugging Face and Meta AI’s model repository
- Using cloud-based or local GPUs for training
Fine-Tuning and Customizing Meta AI LLMs
- Loading pre-trained models
- Fine-tuning on domain-specific datasets
- Transfer learning techniques
Building NLP Applications with Meta AI LLMs
- Developing chatbots and conversational AI
- Implementing text summarization and paraphrasing
- Sentiment analysis and content moderation
Optimizing and Deploying Large Language Models
- Performance tuning for inference speed
- Model compression and quantization techniques
- Deploying LLMs using APIs and cloud platforms
Ethical Considerations and Responsible AI
- Bias detection and mitigation in LLMs
- Ensuring transparency and fairness in AI models
- Future trends and developments in AI
Summary and Next Steps
Requirements
- Basic understanding of machine learning and deep learning
- Experience with Python programming
- Familiarity with natural language processing (NLP) concepts
Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers interested in NLP
21 Hours
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