Understanding Machine Learning
Josh shares insights on understanding machine learning processes, emphasizing the importance of grasping the nuances of neural networks and hyperparameters. He challenges the notion of machine learning as a black box, likening it to understanding how a car functions despite its complexity.In this clip
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Practical AI
Full-stack approach for effective AI agents
Related Questions
Why are neural networks hard to explain?
I'm thinking that classical or deep ML solutions have a flaw in that they cannot be extended. For example, if one builds a model and wants to introduce a new feature, typically the model has to be retrained from scratch. So, I'm considering building a Knowledge Graph using LLMs. This knowledge graph would have to include time-dependent data (for example, it should be able to retrieve the 'current' President of the USA and also previous presidents if asked). I'm thinking this Knowledge Graph could be used for Retrieval Augmented Generation (RAG) to help with business goals or maybe used with more clever User Interfaces (UIs). I'm not sure how to build or populate this KG and also have a rough idea of how to use it. Can you help me with suggesting particular paths for building and populating this Knowledge Graph?
I want to understand what a 'Neural Network' is