t-SNE
t-SNE at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Peek inside a neural network as it learns, layer by layer”, “Explore how AI sees Trump before context enters the picture”, and “Explore how GPT-2 maps tokens with an interactive visual graph.”. Open the training view and you'll watch a small neural network learn to read digits, layer by layer. A single word, "Trump," behaves like two different neighbors depending on how you slice its embedding. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every t-SNE story on Beyond Market Intelligence, newest first.
Peek inside a neural network as it learns, layer by layer
Open the training view and you'll watch a small neural network learn to read digits, layer by layer. Built with plain NumPy and manual backprop, this tool exposes the messy, instructive details most courses skim over: gradient norms, inactive neurons, and weight shifts from initialization. It reaches about 98.5% on MNIST, but the real value is the lab, where you can ablate neurons or tweak temperature and see accuracy move instantly. For teachers and self-learners, it turns abstract theory into something you can touch.
Explore how AI sees Trump before context enters the picture
A single word, "Trump," behaves like two different neighbors depending on how you slice its embedding. In GPT-2 Small's static table, discretizing the coordinates surfaces generic political names like Mitt and Pelosi. Keep the continuous values, and the same token clusters with family, staff, and rivals like Obama and Eisenhower. That gap is a quiet reminder that representation choices shape what models see.
Explore how GPT-2 maps tokens with an interactive visual graph.
There's a quiet thrill in tapping a single token and watching its nearest kin appear. This interactive map of GPT-2's token embedding space makes that possible, offering 32,070 alphabetic tokens to explore with a pinch or a tap. No forward pass, no context, just raw relationships laid bare. The t-SNE layout and minimum spanning tree ensure every line represents a genuine neighbourly bond. It's a hands-on way to grasp how the model organises language, and it works beautifully on mobile.