🤖 Applied AI Study Group #9 – Reflection & Learnings

Applied AI #9 was a selective and immersive study group focused on applying cutting-edge AI techniques in real-world domains like NLP, Computer Vision, and Business ML.

Theoretical Foundation

• ML Engineering & Deployment: prototyping, RESTful ML APIs, MLOps
• NLP with Transformers: HuggingFace, BERT, GPT, fine-tuning, prompt engineering
• Advanced Computer Vision: Diffusion models, ViTs, CLIP, multimodal models
• Industry Talks: AI in healthcare, finance, biology, generative design, career paths

💡 The theory sessions helped me connect abstract concepts with real-world systems, improving my AI intuition.

Hands-On Labs & Projects

• Built ML microservices with FastAPI
• Fine-tuned LLMs on benchmark NLP tasks
• Experimented with diffusion models and CLIP for multimodal tasks
• Tools: Python, PyTorch, HuggingFace, Docker, GitHub, Colab Pro

✅ These labs made complex models tangible—training, debugging, and deploying them reinforced core skills.

Peer Learning & Collaboration

• Weekly peer discussions after lectures
• Debugging and implementation reviews with others
• Participant presentations and shared repos
• Learned from the diverse backgrounds of AI researchers, students, and engineers

💡 It wasn't just learning from the instructors—it was a shared journey of growth with brilliant minds.

Guest Talks & Real-World Inspiration

• AI in Health – Gizem Tanrıver
• Risk Modeling with ML – Oğuz Kaplan
• Alzheimer's Prediction with AI – Elvan Karasu
• Generative Chemistry with LLMs – Onur Boyar

✅ These sessions showed how deep tech meets real impact—and how AI solves meaningful problems across industries.

Community & Networking

• Connected with 15+ passionate AI peers
• Stayed active in inzva's AI community on Slack
• Continued idea exchange and collaboration after the program

💬 These peers are now part of my AI circle—motivating and collaborating even post-program.