
Augmenting LLMs: Fine-Tuning or RAG?
Understanding the tradeoffs between RAG and Fine-tuning, and owning the model vs. using a third-party host.
Understanding the tradeoffs between RAG and Fine-tuning, and owning the model vs. using a third-party host.
Understanding the challenges of traditional fine-tuning and addressing them with LoRA.
Train large deep learning models efficiently.
Understanding every little detail on vector databases and their utility in LLMs, along with a hands-on demo.
The limitations of always using cross-entropy loss in ordinal datasets.
What are we missing here?
Eliminating the dependence of PyTorch models on Python.
The guide that every data scientist must read to manage ML experiments like a pro.
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