Step Into Neural Networks—Grow Your Intuition

Somewhere in the middle of a long afternoon, you might see a participant freeze in front of their notepad—pen hovering—realizing that the “layers” they thought they understood are suddenly operating like a conversation, not a conveyor belt. That’s a telling moment. We see it often. What we’re offering through Jandoris Tromelio’s approach isn’t just another pass through the usual diagrams or rote code exercises. Instead, “it” (that stubborn, fitting word) tries to draw out both the solid lines and the loops: the ways neural networks unfold step by step, and the recursive, sometimes messy process of learning to actually build and use them. Most courses, in my experience, march you forward one tidy module at a time—forward, always forward—until you’re supposed to “get it.” But that’s not really how people learn to work with these architectures. Sometimes, someone will spend a week wrestling with backpropagation, convinced it’s just a matter of memorizing more math, but then—maybe while walking the dog, or tinkering with a half-broken code snippet—something snaps into place. Suddenly, they see how error signals ripple backward, not as an abstract rule, but as a kind of echo that shapes the network’s whole structure. That’s one of the breakthroughs we’ve noticed: the difference between knowing about neural networks and thinking with them. And Jandoris’s response grew from watching where people get stuck. There’s a gap, a sort of quiet canyon, between the way neural networks are explained in textbooks and the wild, iterative, sometimes seat-of-the-pants way they show up in projects that actually matter. A lot of conventional education hammers away at the notion that there’s a “right” way to build these things, or that intuition is just a shortcut for the lazy. In practice, the opposite turns out to be true: you need both the methodical, stepwise habits—the kind you pick up by building something as plain as a single-layer perceptron—and the willingness to circle back, to break your own rules and try again. (On that note, we’ve had more than one participant who, after building a custom convolutional layer from scratch, went off to adapt the same logic for music composition—an application nobody predicted at the outset.) People ask if this means we ignore the fundamentals. Not at all. In fact, one of the challenges we’ve faced is convincing learners that the basics aren’t just “preliminaries” to suffer through before the real work begins; they’re the ground you come back to, again and again. There’s a common misconception that once you’ve coded up your first network, the rest is just stacking blocks. But the architecture itself—how you decide to connect things, which paths allow for recursion, when to let go of a perfectly rational design for something slightly odd—those choices end up mattering more than anyone admits in an introductory lecture. And the way people apply what they’ve learned? That’s as unpredictable as it is satisfying. I remember a participant who, after weeks of frustration, built a network to classify seaweed samples, because she could finally see the structure in the chaos. That’s the kind of thing you can’t plan for, but “it” makes space for those moments.