{
  "id": 5816877,
  "title": "From API to GPU, Week 6 (Part 2): Watching a Neural Network Learn",
  "url": "https://urgent.news/2026/09/05/from-api-to-gpu-week-6-part-2-watching-a-neural-network-learn",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-05T19:11:51.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/dramasamy/from-api-to-gpu-week-6-part-2-watching-a-neural-network-learn-2pef"
  },
  "original_language": "en",
  "account": "Phase 2 of the eight-part series, Week 6 Part 2, explores the neural network learning process. After the first part demonstrated a one-neuron model converting Celsius to Fahrenheit, this post focuses on the learning aspects: weight and bias adjustments, loss minimization, gradient computation, and optimizer steps. The training loop consists of four stages repeated multiple times: forward pass, loss calculation, gradient computation, and optimizer update.",
  "summary": "Phase 2 of 8: Enough ML to understand inference. Week 6 of 32, part 2 of 2. Part 1 built a one-neuron model that converts Celsius to Fahrenheit, made it predict, and scored how wrong it was with a single number, the loss. With random starting values the loss was 10352.21, because every prediction was nonsense. This post takes that one number and turns it into learning: the weight and bias climb…",
  "key_points": [
    "Neural network learning involves weight and bias adjustments",
    "Loss is minimized through gradient computation",
    "Optimizer updates occur during training loop stages"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}