Knowledge Graphs for RAG - DeepLearning.AI
Use Neo4j’s query language Cypher to manage and retrieve data stored in knowledge graphs. Write knowledge graph queries that find and format text data to provide more relevant context to LLMs for Retrieval Augmented Generation. Build a question-answering system using Neo4j and LangChain to chat with a knowledge graph of structured text documents. Knowledge graphs are used in development to structu
Building and Evaluating Advanced RAG Applications - DeepLearning.AI
Learn methods like sentence-window retrieval and auto-merging retrieval, improving your RAG pipeline’s performance beyond the baseline. Learn evaluation best practices to streamline your process, and iteratively build a robust system. Dive into the RAG triad for evaluating the relevance and truthfulness of an LLM’s response:Context Relevance, Groundedness, and Answer Relevance. Retrieval Augmented
AI Canon | Andreessen Horowitz
Research in artificial intelligence is increasing at an exponential rate. It’s difficult for AI experts to keep up with everything new being published, and even harder for beginners to know where to start. So, in this post, we’re sharing a curated list of resources we’ve relied on to get smarter about modern AI. We call it the “AI Canon” because these papers, blog posts, courses, and guides have h
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GitHub - oreilly-japan/practical-dl-for-cloud-mobile-edge-ja
GitHub - PracticalDL/Practical-Deep-Learning-Book: Official code repo for the O'Reilly Book - Practical Deep Learning for Cloud, Mobile & Edge
