Classic CNN Architectures

Once the core CNN operations are clear, it becomes easier to read architectures as design choices rather than as a list of names. Two models are especially important:

  • LeNet, which showed that convolutional networks could work in practice for recognition tasks,
  • AlexNet, which pushed deep CNNs into the center of modern computer vision.

LeNet, developed by Yann LeCun and collaborators, is one of the foundational CNN architectures. It combines:

  • convolutional layers,
  • pooling layers,
  • and fully connected layers.

Its importance is not just historical. It established the basic architectural rhythm that still appears in many later CNNs: extract local features, compress spatially, then classify.

AlexNet marked a major turning point in 2012 when it won the ImageNet competition by a large margin.

Its impact came from combining several ideas effectively:

  • deeper convolutional stacks,
  • ReLU activations,
  • dropout regularization,
  • large-scale data,
  • and GPU-based training.

AlexNet did not invent every component it used, but it showed that the combination could outperform older computer-vision pipelines decisively.

LeNet and AlexNet illustrate a broader lesson:

architecture progress usually comes from combining sound building blocks with enough data, compute, and training discipline.

That same pattern continues in later deep-learning families far beyond CNNs.

In this lesson we covered:

  1. Why classic architectures matter for understanding modern CNN design
  2. The role of LeNet in establishing practical CNN structure
  3. The role of AlexNet in the deep-learning breakthrough era
  4. The architectural ideas that made AlexNet influential
  5. The broader lesson linking architecture, optimization, and scale

This completes the deep-learning section. Future lessons will extend it toward sequence models, attention, and generative models.