The question behind the work.
How do layers, activations, and differentiable losses combine into a trainable model?
Dataset & environment
Educational neural-network architecture explorer included on this site.
From the idea to the method.
- Build the forward-pass computation from linear transformations and activation functions.
- Express a loss, compute gradients, and apply a parameter update.
- Study layer width, depth, model capacity, and validation behavior.
A training step
optimizer.zero_grad()
prediction = model(inputs)
loss = criterion(prediction, targets)
loss.backward()
optimizer.step()Backpropagation accumulates derivatives of the loss with respect to each parameter. The optimizer then updates those parameters. The architecture explorer above changes layer structure and the parameter count, independently of any training experiment.
What this study explores.
- Academic focus: forward propagation, backpropagation, and numerical optimization.
- Conceptual connection between gradient descent and practical model training.
How to assess it carefully.
Check gradient shapes, compare analytic gradients with finite differences on small examples, and monitor validation loss for concrete experiments.
What is documented.
Experimental artifacts and verified numerical results have not been published. The interactive visuals explain the method; they are not benchmark results.
Training curves, accuracy comparisons, and repository links will be added alongside the corresponding artifacts. No unverified benchmark numbers are shown.
A foundation for the next experiment.
- Capacity and optimization behavior are different questions.
- A reliable evaluation split is part of the model design.
Future updates can include code, screenshots, reproducibility instructions, and verified outcomes.