Yapay Zeka Mühendisliği (7. Ders) (İnce Ayar - Finetuning)

In this video, episode 7 of our Artificial Intelligence Engineering series, we discuss Finetuning. We explain when finetuning is necessary to make large language models more suitable for a specific task, domain, or output format, when it can create unnecessary costs, and how it compares to RAG (Real-Time Agility). Key topics covered in the video: What is finetuning? Its relationship to transfer learning Types of finetuning When should finetuning be done, and when shouldn't it be done? RAG vs. finetuning? Memory bottlenecks and training costs FP32, BF16, FP16, INT8, INT4 representations PEFT, LoRA, and QLoRA approaches Why is LoRA so popular? - Model merging / federated learning #ArtificialIntelligence #Finetuning #LLM #LargeLanguageModels #ArtificialIntelligenceEngineering #RAG #LoRA #QLoRA #PEFT #MachineLearning #DeepLearning #GenerativeAI #PromptEngineering #ArtificialIntelligence #AIEngineering #LargeLanguageModels