Description
Introduction of Large Language Models:
Large Language Models (LLMs), such as GPT and BERT, represent a leap in the capabilities of artificial intelligence, enabling machines to understand, generate, and manipulate human language. This training course is designed to introduce participants to LLMs, covering their underlying architecture, use cases, and applications in various industries. Attendees will gain insights into how these models work, how to fine-tune them for specific tasks, and the ethical considerations of using AI-driven language technologies.
Prerequisites:
- Basic understanding of machine learning concepts.
- Familiarity with Python programming.
- Knowledge of neural networks and deep learning frameworks (e.g., TensorFlow, PyTorch) is recommended but not mandatory.
Table of Contents
1: Introduction
1.1 Overview of NLP and the evolution of language models
1.2 Introduction: GPT, BERT, and others
1.3 Key concepts: Transformers, Attention Mechanism, and Self-Attention
1.4 Understanding pre-training and fine-tuning
1.5 Hands-on: Exploring LLMs with OpenAI’s GPT models
2: Architecture and Working of LLMs
2.1 Dive into the Transformer architecture
2.2 The role of tokens, embeddings, and positional encoding
2.3 Training large language models: dataset collection, tokenization, and optimization techniques
2.4 Fine-tuning large language models for specific tasks (e.g., translation, summarization)
2.5 Hands-on: Fine-tuning an LLM using Hugging Face
3: Applications
3.1 Use cases across industries: healthcare, finance, customer support, etc.
3.2 Text generation and summarization
3.3 Chatbots and conversational AI
3.4 Language translation and text classification
3.5 Hands-on: Building a chatbot using GPT
4: Advanced Techniques and Ethical Considerations
4.1 Scaling models: Efficient use of resources
4.2 Techniques for improving LLM performance
4.3 Addressing bias in large language models
4.4 Ethical concerns: Data privacy, bias, and misinformation
4.5 Hands-on: Exploring model interpretability and debiasing techniques
5: Future Directions and Challenges
5.1 Next-generation LLMs: Multimodal models, GPT-4, and beyond
5.2 The role of reinforcement learning in LLMs
5.3 Challenges: Scalability, resource efficiency, and deployment
5.4 Trends in research and development in LLMs
5.5 Hands-on: Experimenting with a state-of-the-art LLM
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Each session includes practical, hands-on activities designed to reinforce the theoretical concepts and demonstrate real-world applications of large language models.
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