Gen AI Course | Gen AI Tutorial For Beginners
codebasics・2 minutes read
The text discusses the fundamentals of Generative AI, highlighting projects using commercial and open-source models, the evolution of AI from statistical to deep learning, Transformers, vector databases, embeddings, and the Lang chain framework for AI applications, emphasizing the importance of building secure and efficient solutions for real-world industry applications. It also details the process of using Lang chain to load, split, embed, and retrieve text data, along with creating applications like a restaurant name generator and a news research tool for equity analysts by combining various components like loaders, splitters, embeddings, and retrieval methods efficiently.
Insights
- Generative AI creates new content, unlike non-generative AI that uses existing data for decision-making.
- Large language models (LLMs) trained on extensive datasets like Wikipedia enable predictive text generation.
- Vector databases offer faster search capabilities and optimal storage, becoming popular for AI applications.
- Agents in Lang chain connect with external tools like Google Search and Wikipedia, enhancing AI models' reasoning capabilities.
- Lang chain aids in building real-life industry applications like a news research tool for equity analysts using semantic search techniques.
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What is Generative AI?
Generative AI creates new content.
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