Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)
54:52
BERT explained: Training, Inference, BERT vs GPT/LLamA, Fine tuning, [CLS] token
50:55
Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training
27:12
Variational Autoencoder - Model, ELBO, loss function and maths explained easily!
16:38
The architecture and capsule design of IDEA4RC for federated secure health data processing
1:19:27
Stanford CS25: V3 I Retrieval Augmented Language Models
26:55
LoRA: Low-Rank Adaptation of Large Language Models - Explained visually + PyTorch code from scratch
15:21
Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use
58:04