📅 Date: 6 April

⏰ Time: 4PM

🌐 Location: Virtual

What’s Happening This Week?

We’ll introduce the concept of Domain-Specific LLMs, their benefits, and their applications. 

After the session, participants will continue with self-paced learning to deepen their understanding and explore hands-on exercises for domain-specific modeling.

About the Session: Domain-Specific LLMs

Topics Covered in the Session:

1️⃣ Introduction to Domain-Specific LLMs – Learn what makes these models different from general-purpose LLMs.

2️⃣ Customization Techniques – Explore how to fine-tune and adapt pre-trained LLMs for specific industries or use cases, such as:

• Healthcare (e.g., Med-PaLM)
• Finance (e.g., FinBERT)
• Security (e.g., SecLM)

3️⃣ Data Requirements – Understand the importance of domain-specific datasets for training and validation.

4️⃣ Applications – Learn about real-world use cases, such as:

• Medical diagnosis and assistance
• Financial forecasting and risk assessment
• Cybersecurity threat analysis

5️⃣ Challenges and Tradeoffs – Address potential challenges, such as dataset availability, bias, and overfitting.

Session Highlights:

• Best practices for fine-tuning LLMs for specific domains
• Tools and frameworks for customization (e.g., Hugging Face, PyTorch, TensorFlow)
• Insights from experts and case studies on domain-specific applications

Requisites for Week 8:

1️⃣ Completion of Week 7’s Generative AI Agents session 📚

2️⃣ Familiarity with foundational LLM concepts and embeddings (from earlier weeks) 🌐

3️⃣ Access to domain-specific datasets for hands-on experimentation ✅

This session will equip participants with the knowledge and tools to harness the power of LLMs for industry-specific challenges, opening doors to AI innovation in specialized fields.

Complete Series Overview

This program unfolds across multiple weeks, offering foundational insights and advanced explorations into machine learning and generative AI.

ML Study Jam Weekly Plan:

1️⃣ Week 1: Intro to Machine Learning

2️⃣ Week 2: Intermediate Machine Learning

3️⃣ Week 3: Intro to Deep Learning

4️⃣ Week 4: Intro to AI Ethics

5️⃣ Week 5: Foundational Models & Prompt Engineering

6️⃣ Week 6: Embeddings and Vector Stores/Databases

7️⃣ Week 7: Generative AI Agents

8️⃣ Week 8: Domain-Specific LLMs

9️⃣ Week 9: MLOps for Generative AI

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