Neural Networks & Backpropagation Intuition
Every deep-learning model is trained by backpropagation, so a mental model of it prevents blind copy-paste later.
Learn it
Watch 3Blue1Brown build visual intuition for what a network computes and how gradient descent nudges weights. Then follow Karpathy building a tiny autograd engine so backprop stops being magic. StatQuest reinforces the chain-rule mechanics with worked arithmetic.
By the end you’ll be able to
- Explain how layered neurons compute a forward pass
- Describe how gradients flow backward via the chain rule
Free resources
- 3Blue1Brown — Neural Networks (playlist)(opens in a new tab)Start here
The clearest visual intuition for networks and gradient descent.
Free~1 hr - Andrej Karpathy — Building micrograd (backprop from scratch)(opens in a new tab)
Code an autograd engine from scratch alongside the video.
Free2h25m - StatQuest — Neural Networks Pt. 2: Backpropagation(opens in a new tab)
Bite-sized reinforcement of the chain-rule mechanics.
Free~18 min
Practice
Recreate a 2-layer network by hand (or in Karpathy's micrograd) and manually compute one backprop update for a single training example.
Build it — your output
A short notebook or notes file tracing one forward and backward pass with the numeric gradients you computed.
Prove it — check yourself
You’re ready to move on when you can honestly say yes to each:
- Can you state why nonlinearity is required between layers?
- Can you explain what a gradient tells you about a weight?
- Can you walk through one chain-rule step from loss back to a weight?
Do it with AI
Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).
The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.
- Understand itClaude / ChatGPT / Gemini
I'm a beginner learning "Neural Networks & Backpropagation Intuition". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.
- PracticeClaude / ChatGPT / Gemini
Give me 3 short practice problems on "Neural Networks & Backpropagation Intuition" (my goal: explain how layered neurons compute a forward pass), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.
- Get unstuckClaude / ChatGPT / Gemini
I'm stuck on this exercise about "Neural Networks & Backpropagation Intuition": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.
- Build & reviewCursor / Copilot
Here is my work for a "Neural Networks & Backpropagation Intuition" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.
- Prove itClaude / ChatGPT / Gemini
Quiz me on "Neural Networks & Backpropagation Intuition". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.