Embark on a transformative journey with EdAthena's Post Graduate Program Specialization in Language Models (LLMs). Tailor your education with a range of specializations and electives, complemented by real-world case studies to enhance practical learning. This program is divided into three phases, each lasting five months, designed to provide comprehensive knowledge and skills in various aspects of language models.
The Post Graduate Program Specialization in Language Models (LLMs) at EdAthena is designed to equip you with the essential skills required to excel in the field of language models and generative AI. Through comprehensive modules, hands-on projects, and real-world case studies, you will gain a deep understanding of LLM techniques, including transformer architectures, prompt engineering, and fine-tuning for specific tasks. Each phase of the program builds on the previous one, ensuring a seamless learning experience and a robust foundation in LLMs. The curriculum covers essential topics such as deploying LLMs in production, ethical considerations, and building LLM-powered applications. By the end of the program, you will be well-equipped to develop, deploy, and manage advanced language models, and apply your knowledge to create innovative AI solutions in various domains.
Comprehensive Curriculum: The program covers essential topics in language models, including transformer architectures, prompt engineering, and domain-specific LLMs, ensuring a robust foundation in LLM techniques.
Hands-On Learning: Emphasizes practical learning through real-world case studies and hands-on projects, allowing participants to apply theoretical knowledge to practical scenarios.
Specializations and Electives: Offers a range of specializations and electives, enabling participants to tailor their education to their specific interests and career goals, focusing on the latest trends and innovations in LLMs.
A. Introduction to Language Models
• Overview of Language Models
• Types and Applications of Language Models
B. Transformer Architecture and Pre-training
• Transformer Architecture
• Pre-training Techniques for LLMs
C. Prompt Engineering and Model Evaluation
• Prompt Engineering Techniques
• Zero-Shot and Few-Shot Learning
A. GPT and Beyond
• GPT Architecture and Capabilities
• Fine-Tuning GPT for Specific Tasks
B. Multilingual and Domain-Specific LLMs
• Multilingual Language Models
• Domain-Specific LLMs (e.g., Legal, Medical, Scientific)
A. Deploying LLMs
• Deploying LLMs in Production Environments
• Serving LLMs with APIs and Microservices
B. Generative AI Ethics and Responsible AI
• Techniques for Scaling LLMs
• Model Compression and Quantization
C. Capstone Project and Emerging Trends
• Building an LLM-Powered Application
• Integrating LLMs with Other AI Components
