Classic CNN Architectures

Over the years, a variety of CNN (Convolutional Neural Network) architectures have been developed, each contributing to advancements in the field of deep learning and its applications. Among these, some of the most renowned architectures include:

• LeNet-5: One of the earliest convolutional neural networks, pivotal for handwriting recognition.
• AlexNet: A breakthrough architecture that significantly outperformed competitors in the ImageNet challenge.
• VGGNet: Known for its simplicity and depth, with a focus on increasing the depth of the network using small filters.
• GoogLeNet : GoogLeNet: Introduced the inception module, enabling the network to choose from filters of various sizes.
• ResNet: Introduced residual connections to facilitate training of very deep networks, solving the vanishing gradient problem.

To better understand the unique architectures and contributions of these CNNs, watch the following video: