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Building LLM-Powered Recommendation Systems

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Building LLM-Powered Recommendation Systems

Torrent Contents Size: 251.4 MB

Building LLM-Powered Recommendation Systems
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01. Introduction
01. Discover the power of generative AI for recommendation systems.mp4
MP4
1.8 MB
01. Discover the power of generative AI for recommendation systems.srt
SRT
1.2 KB
02. 1. High-Impact GenAI Enhancements for Recommenders
01. Choosing your GenAI tool LLMs, GANs, and diffusion.mp4
MP4
11.4 MB
01. Choosing your GenAI tool LLMs, GANs, and diffusion.srt
SRT
8.2 KB
02. Creating quality embeddings with sentence transformers.mp4
MP4
15.5 MB
02. Creating quality embeddings with sentence transformers.srt
SRT
9.8 KB
03. 2. Architecting GenAI-Native Recommender Systems
01. Foundational follow-up How LLMs understand—A primer on the transformer.mp4
MP4
7.7 MB
01. Foundational follow-up How LLMs understand—A primer on the transformer.srt
SRT
6 KB
02. The generative retrieval architecture.mp4
MP4
7.6 MB
02. The generative retrieval architecture.srt
SRT
6.1 KB
03. The key component Semantic item tokenization.mp4
MP4
7.6 MB
03. The key component Semantic item tokenization.srt
SRT
5.8 KB
04. 3. Evaluating GenAI Recommenders Quality, Fairness, and Trust
01. Evaluating recommendation lists Diversity and novelty.mp4
MP4
9 MB
01. Evaluating recommendation lists Diversity and novelty.srt
SRT
6.7 KB
02. Evaluating generated text ROUGE, BLEU, and BERTScore.mp4
MP4
15.6 MB
02. Evaluating generated text ROUGE, BLEU, and BERTScore.srt
SRT
9.4 KB
03. The RAG architecture A deep dive into factual grounding.mp4
MP4
7.5 MB
03. The RAG architecture A deep dive into factual grounding.srt
SRT
6.2 KB
04. Red teaming Proactively finding failure modes.mp4
MP4
8.5 MB
04. Red teaming Proactively finding failure modes.srt
SRT
6.1 KB
05. 4. Operationalizing GenAI Recommender Systems at Scale
01. Production infrastructure Vector databases and model serving.mp4
MP4
9.9 MB
01. Production infrastructure Vector databases and model serving.srt
SRT
7.1 KB
02. Foundational follow-up The two-tower model.mp4
MP4
9.6 MB
02. Foundational follow-up The two-tower model.srt
SRT
7.1 KB
03. Monitoring for embedding drift and quality degradation.mp4
MP4
8.7 MB
03. Monitoring for embedding drift and quality degradation.srt
SRT
7 KB
04. Scaling for inference Quantization and knowledge distillation.mp4
MP4
20.4 MB
04. Scaling for inference Quantization and knowledge distillation.srt
SRT
12.5 KB
06. 5. Conclusion
01. Course summary.mp4
MP4
10.1 MB
01. Course summary.srt
SRT
6.2 KB
02. The future is agentic Designing recommenders as autonomous agents.mp4
MP4
10.6 MB
02. The future is agentic Designing recommenders as autonomous agents.srt
SRT
6.2 KB
04. Building conversational recommenders with tool use and RAG.mp4
MP4
8.7 MB
04. Building conversational recommenders with tool use and RAG.srt
SRT
6.2 KB
05. Multimodal fusion How cross-attention works.mp4
MP4
8.7 MB
05. Multimodal fusion How cross-attention works.srt
SRT
5.4 KB
06. Architectural challenge Managing long-term user memory.mp4
MP4
8.8 MB
06. Architectural challenge Managing long-term user memory.srt
SRT
5.7 KB
03. Foundational follow-up The core shift—From item IDs to semantic embeddings.mp4
MP4
7.5 MB
03. Foundational follow-up The core shift—From item IDs to semantic embeddings.srt
SRT
6 KB
04. Summarizing user history for better personalization.mp4
MP4
13 MB
04. Summarizing user history for better personalization.srt
SRT
9.4 KB
05. Solving item cold-start with cross-modal embeddings.mp4
MP4
7.8 MB
05. Solving item cold-start with cross-modal embeddings.srt
SRT
6.2 KB
06. Few-shot prompting for personalized explanations.mp4
MP4
13.7 MB
06. Few-shot prompting for personalized explanations.srt
SRT
8.8 KB
07. Data augmentation Creating hard negatives with LLMs.mp4
MP4
12.3 MB
07. Data augmentation Creating hard negatives with LLMs.srt
SRT
8.8 KB
08. Foundational follow-up Augment vs. replace—The LLMERS production pattern.mp4
MP4
9.3 MB
08. Foundational follow-up Augment vs. replace—The LLMERS production pattern.srt
SRT
7.6 KB

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