Building LLM-Powered Recommendation Systems

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• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 01. Introduction
• 01. Discover the power of generative AI for recommendation systems.mp4 (1.8 MB)
• 01. Discover the power of generative AI for recommendation systems.srt (1.2 KB)
02. 1. High-Impact GenAI Enhancements for Recommenders
• 01. Choosing your GenAI tool LLMs, GANs, and diffusion.mp4 (11.4 MB)
• 01. Choosing your GenAI tool LLMs, GANs, and diffusion.srt (8.2 KB)
• 02. Creating quality embeddings with sentence transformers.mp4 (15.5 MB)
• 02. Creating quality embeddings with sentence transformers.srt (9.8 KB)
• 03. Foundational follow-up The core shift—From item IDs to semantic embeddings.mp4 (7.5 MB)
• 03. Foundational follow-up The core shift—From item IDs to semantic embeddings.srt (6.0 KB)
• 04. Summarizing user history for better personalization.mp4 (13.0 MB)
• 04. Summarizing user history for better personalization.srt (9.4 KB)
• 05. Solving item cold-start with cross-modal embeddings.mp4 (7.8 MB)
• 05. Solving item cold-start with cross-modal embeddings.srt (6.2 KB)
• 06. Few-shot prompting for personalized explanations.mp4 (13.7 MB)
• 06. Few-shot prompting for personalized explanations.srt (8.8 KB)
• 07. Data augmentation Creating hard negatives with LLMs.mp4 (12.3 MB)
• 07. Data augmentation Creating hard negatives with LLMs.srt (8.8 KB)
• 08. Foundational follow-up Augment vs. replace—The LLMERS production pattern.mp4 (9.3 MB)
• 08. Foundational follow-up Augment vs. replace—The LLMERS production pattern.srt (7.6 KB)
03. 2. Architecting GenAI-Native Recommender Systems
• 01. Foundational follow-up How LLMs understand—A primer on the transformer.mp4 (7.7 MB)
• 01. Foundational follow-up How LLMs understand—A primer on the transformer.srt (6.0 KB)
• 02. The generative retrieval architecture.mp4 (7.6 MB)
• 02. The generative retrieval architecture.srt (6.1 KB)
• 03. The key component Semantic item tokenization.mp4 (7.6 MB)
• 03. The key component Semantic item tokenization.srt (5.8 KB)
• 04. Building conversational recommenders with tool use and RAG.mp4 (8.7 MB)
• 04. Building conversational recommenders with tool use and RAG.srt (6.2 KB)
• 05. Multimodal fusion How cross-attention works.mp4 (8.7 MB)
• 05. Multimodal fusion How cross-attention works.srt (5.4 KB)
• 06. Architectural challenge Managing long-term user memory.mp4 (8.8 MB)
• 06. Architectural challenge Managing long-term user memory.srt (5.7 KB)
04. 3. Evaluating GenAI Recommenders Quality, Fairness, and Trust
• 01. Evaluating recommendation lists Diversity and novelty.mp4 (9.0 MB)
• 01. Evaluating recommendation lists Diversity and novelty.srt (6.7 KB)
• 02. Evaluating generated text ROUGE, BLEU, and BERTScore.mp4 (15.6 MB)
• 02. Evaluating generated text ROUGE, BLEU, and BERTScore.srt (9.4 KB)
• 03. The RAG architecture A deep dive into factual grounding.mp4 (7.5 MB)
• 03. The RAG architecture A deep dive into factual grounding.srt (6.2 KB)
• 04. Red teaming Proactively finding failure modes.mp4 (8.5 MB)
• 04. Red teaming Proactively finding failure modes.srt (6.1 KB)
05. 4. Operationalizing GenAI Recommender Systems at Scale
• 01. Production infrastructure Vector databases and model serving.mp4 (9.9 MB)
• 01. Production infrastructure Vector databases and model serving.srt (7.1 KB)
• 02. Foundational follow-up The two-tower model.mp4 (9.6 MB)
• 02. Foundational follow-up The two-tower model.srt (7.1 KB)
• 03. Monitoring for embedding drift and quality degradation.mp4 (8.7 MB)
• 03. Monitoring for embedding drift and quality degradation.srt (7.0 KB)
• 04. Scaling for inference Quantization and knowledge distillation.mp4 (20.4 MB)
• 04. Scaling for inference Quantization and knowledge distillation.srt (12.5 KB)
06. 5. Conclusion
• 01. Course summary.mp4 (10.1 MB)
• 01. Course summary.srt (6.2 KB)
• 02. The future is agentic Designing recommenders as autonomous agents.mp4 (10.6 MB)
• 02. The future is agentic Designing recommenders as autonomous agents.srt (6.2 KB)
• Bonus Resources.txt (0.1 KB)