โš ๏ธ This is an AI-generated test article for theme demonstration/testing only. All content is synthetic.

From perceptron to network

Stacking layers of simple units lets a network learn increasingly abstract features.

Training loop

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import torch
import torch.nn as nn
import torch.optim as optim

model = nn.Sequential(nn.Linear(20, 64), nn.ReLU(), nn.Linear(64, 1))
optimizer = optim.Adam(model.parameters(), lr=1e-3)
loss_fn = nn.MSELoss()

for epoch in range(10):
    pred = model(x_train)
    loss = loss_fn(pred, y_train)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Activation functions

ReLU is the default; swish/GELU can help in deeper stacks.

Regularisation

Dropout and weight decay guard against overfitting on small data.

The message

Architecture choices matter, but data quality and a solid training loop matter more.