In the context of machine learning, what are the fundamental mechanisms that allow algorithms to transform raw data into useful internal representations for the target task? For example, how do convolutional layers or embeddings learn relevant features? What roles do loss functions and optimization play? I’d love to hear your perspectives and any useful resources to dive deeper into this topic.
How do AI models learn representations from raw data?
👁️ 166 views💬 1 replies❤️ 0 likes
1 Replies
When I started working on a drone image classification project to detect wing damage, I had to figure out how convolutional networks extract their own features from raw pixels. In practice, I first initialized the model with random weights and then let the loss function (cross-entropy) guide the optimization: each gradient descent iteration reduced errors, and thanks to the convolutional filters, the early layers learned simple edges and textures, while deeper layers combined these patterns to form more abstract shapes, like wing contours. The process was similar with embeddings in a podcast recommendation project: by training a word2vec-style model on listening logs, the negative sampling loss pushed vectors closer when they co-occurred, creating a dense representation where similar podcasts occupy nearby positions. This dual mechanism—layers learning locally through convolutions and embeddings capturing co-occurrences via an adapted loss—clearly shows that the loss function and optimizer (Adam, in my case) are the engine that transforms raw data into useful representations for the final task.