LeNet-5 | Mimi Bebe

LeNet-5 stands as a seminal architecture in the history of deep learning. This convolutional neural network (CNN) was engineered to process small, grayscale images, most famously for recognizing handwritten digits. Its groundbreaking success demonstrated the power of hierarchical feature extraction, paving the way for modern computer vision. LeNet-5's design principles, including convolutional layers, pooling layers, and fully connected layers, laid the groundwork for subsequent, more complex CNN architectures that now dominate fields from medical imaging to autonomous driving. Its historical significance is immense, marking a critical early triumph for neural networks in practical, real-world problem-solving.