Classification

#Goal of the lesson

By the end of this 3-hour session you should be able to:

• explain the difference between regression and classification,
• generate synthetic 2-D datasets and visualize their decision regions,
• build a feed-forward neural network with non-linear activations,
• choose the right loss for binary and multi-class problems,
• track loss and accuracy during training,
• recognize underfitting and overfitting visually,
• handle real-world tabular data with mixed numerical and categorical features,
• solve the moons dataset as a capstone.

#Suggested timing

#Regression vs. classification

The five-step workflow doesn’t change. We swap the dataset, the model’s output size, and the loss.

#Setup

uv init --python 3.12 classification
cd classification
uv add torch matplotlib scikit-learn numpy

import matplotlib.pyplot as plt
import numpy as np
import torch
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from torch import nn

device = "cuda" if torch.cuda.is_available() else "cpu"
torch.manual_seed(42)

#Multi-class — the blobs dataset

sklearn.datasets.make_blobs generates clusters of points in 2-D — perfect for visualizing what a classifier is doing.

NUM_CLASSES = 4
NUM_FEATURES = 2

x_np, y_np = make_blobs(
    n_samples=1000,
    n_features=NUM_FEATURES,
    centers=NUM_CLASSES,
    cluster_std=1.5,
    random_state=42,
)

x = torch.from_numpy(x_np).float()
y = torch.from_numpy(y_np).long()

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

print(x_train.shape, y_train.shape, y_train[:10])

A few details that matter:

• Features are float32. Targets for CrossEntropyLoss must be int64 (the dtype .long() produces).
• Targets are class indices (0, 1, 2, 3), not one-hot vectors. PyTorch’s loss does the one-hot conversion internally.

Visualize:

plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.RdYlBu, s=8)
plt.title("blobs")
plt.show()