Part 7/15:
CNNs revolutionized how machines process visual data by automatically learning hierarchical features—from simple edges to complex object parts. Researchers visualized kernels in early layers of CNNs revealing detectors for edges, textures, and even specific objects like humans and animals in high-level layers. This paradigm shift proved that neural networks could learn features directly from raw data, eliminating the need for handcrafted features.
Initially designed for images, CNN architectures proved versatile, extending to audio processing via spectrograms, and even to biological signals like ECGs. This demonstrated that neural models could adapt across modalities, further expanding AI's frontier.