Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)
⏱ 49:24 | 👁 87 mil visualizações | 🗓 2 years ago
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Is RAG Still Needed? Choosing the Best Approach for LLMs
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BERT explained: Training, Inference, BERT vs GPT/LLamA, Fine tuning, [CLS] token
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Hyper-RAG Mitigating LLM Hallucinations via Hypergraph Driven Retrieval Augmented Generation
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Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code
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RAG Crash Course for Beginners
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RAG vs. CAG: Solving Knowledge Gaps in AI Models
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Mistral / Mixtral Explained: Sliding Window Attention, Sparse Mixture of Experts, Rolling Buffer
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Distributed Persistent Queues & MySQL Extensibility
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Activation-Informed Merging of LLMs | Amin Heyrani Nobari, Kaveh Alim | Random Samples
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Vector Search & Approximate Nearest Neighbors (ANN) | FAISS (HNSW & IVF)
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Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use
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1:10:55
LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU
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