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Duration 21 hours
Course Outline
Introduction to Large Language Models
- Overview of Natural Language Processing (NLP)
- Introduction to Large Language Models (LLMs)
- Meta AI's role in advancing LLM development
Exploring the Architecture of Meta AI LLMs
- Transformer architecture and self-attention mechanisms
- Training methodologies for large-scale models
- Comparative analysis with other LLMs (GPT, BERT, T5, etc.)
Configuring the Development Environment
- Installation and setup of Python and Jupyter Notebook
- Utilizing Hugging Face and Meta AI’s model repository
- Leveraging cloud-based or local GPUs for training
Fine-Tuning and Customizing Meta AI LLMs
- Loading pre-trained models
- Fine-tuning on domain-specific datasets
- Application of transfer learning techniques
Creating NLP Applications with Meta AI LLMs
- Developing chatbots and conversational AI systems
- Implementing text summarization and paraphrasing tools
- Performing sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Performance tuning to enhance inference speed
- Techniques for model compression and quantization
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Detecting and mitigating bias in LLMs
- Ensuring transparency and fairness in AI models
- Exploring future trends and developments in AI
- Transformer architecture and self-attention mechanisms
- Training methodologies for large-scale models
- Comparative analysis with other LLMs (GPT, BERT, T5, etc.)
Configuring the Development Environment
- Installation and setup of Python and Jupyter Notebook
- Utilizing Hugging Face and Meta AI’s model repository
- Leveraging cloud-based or local GPUs for training
Fine-Tuning and Customizing Meta AI LLMs
- Loading pre-trained models
- Fine-tuning on domain-specific datasets
- Application of transfer learning techniques
Creating NLP Applications with Meta AI LLMs
- Developing chatbots and conversational AI systems
- Implementing text summarization and paraphrasing tools
- Performing sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Performance tuning to enhance inference speed
- Techniques for model compression and quantization
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Detecting and mitigating bias in LLMs
- Ensuring transparency and fairness in AI models
- Exploring future trends and developments in AI
- Loading pre-trained models
- Fine-tuning on domain-specific datasets
- Application of transfer learning techniques
Creating NLP Applications with Meta AI LLMs
- Developing chatbots and conversational AI systems
- Implementing text summarization and paraphrasing tools
- Performing sentiment analysis and content moderation
Optimization and Deployment of Large Language Models
- Performance tuning to enhance inference speed
- Techniques for model compression and quantization
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Detecting and mitigating bias in LLMs
- Ensuring transparency and fairness in AI models
- Exploring future trends and developments in AI
- Performance tuning to enhance inference speed
- Techniques for model compression and quantization
- Deploying LLMs via APIs and cloud platforms
Ethical Considerations and Responsible AI
- Detecting and mitigating bias in LLMs
- Ensuring transparency and fairness in AI models
- Exploring future trends and developments in AI
Requirements
- Fundamental knowledge of machine learning and deep learning
- Practical experience with Python programming
- Knowledge of natural language processing (NLP) concepts
Target Audience
- AI Researchers
- Data Scientists
- Machine Learning Engineers
- Software Developers with an interest in NLP
