| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~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. 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 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. 3. Evaluating GenAI Recommenders Quality, Fairness, and Trust | |||
| 01. Evaluating recommendation lists Diversity and novelty.mp4 | 9 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 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 | ||
| 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 | ||
| 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 KB | ||
| 04. Summarizing user history for better personalization.mp4 | 13 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 |
Building LLM-Powered Recommendation Systems
https://WebToolTip.com
Released 2/2026
With Rishabh Misra
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 18m | Size: 252 MB
Learn how to design, build, evaluate, and deploy production-ready recommender systems that leverage the power of GenAI for enhanced personalization, quality, and user trust.
Course details
Get a technically grounded overview of how to start building the next generation of intelligent recommender systems. Moving beyond traditional algorithms, this course shows you how to immediately enhance existing systems by applying AI-powered techniques for embedding generation, semantic reranking, cold start mitigation, and more. Instructor Rishabha Misra outlines the essentials of designing sophisticated, GenAI-native architectures that enable dynamic experiences like conversational search and multimodal recommendations. An ideal fit for software engineers, data scientists, AI and ML engineers, and technical product managers, this course focuses on robust evaluation, teaching you how to measure for quality and fairness and ensure factual accuracy through patterns like retrieval-augmented generation (RAG). By the end of this course, you’ll be prepared to design, evaluate, and operationalize effective and responsible GenAI recommender systems in a production environment.
Skills covered
Large Language Models (LLM), Recommender Systems
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