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

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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