REDDIT · REDDIT// 8d agoTUTORIAL

Day 1 Kicks Off Neural Network Primer

This Reddit post opens a 30-day learning series on building a small language model by starting with the basics of neural networks. It explains layers, weights, bias, activation functions, loss, and backpropagation using simple cat-vs-dog intuition, then connects that foundation to how language models train with next-token prediction and gradient updates.

// ANALYSIS

Hot take: it is a solid beginner-friendly primer, but it deliberately stays conceptual and uses analogies that simplify the math a lot.

–Clear introduction to the core neural-network vocabulary: input, hidden, output layers, weights, bias, activation, and loss.
–Backpropagation is explained in plain language, which makes the training loop approachable for readers new to ML.
–The bridge to language models is useful: it frames LLMs as the same optimization story with text tokens instead of image labels.
–Best read as a foundation-builder for the rest of the series, not as a technical deep dive or implementation guide.

// TAGS

neural-networksbackpropagationllmlanguage-modelspytorcheducation

DISCOVERED

8d ago

2026-04-04

PUBLISHED

8d ago

2026-04-04

RELEVANCE

7/ 10

AUTHOR

Prashant-Lakhera