convolutional neural network (CNN)

A convolutional neural network (CNN) is a neural network architecture that employs local receptive fields and weight-sharing filters to process structured data, such as images, producing feature maps that are (approximately) translation equivariant.

After early work like LeNet in handwriting recognition and the breakthrough AlexNet at the 2012 ImageNet challenge, CNNs became the dominant approach in vision, until vision transformers overtook them on many benchmarks in the 2020s. Variants now extend to audio, video, and other grid-structured domains.

Compared to fully connected networks, their local structure and parameter sharing improve sample efficiency, computational costs, and generalization on grid-like inputs.

Related Resources

Course

Python Deep Learning: PyTorch vs Tensorflow

PyTorch vs Tensorflow: Which one should you use? Learn about these two popular deep learning libraries and how to choose the best one for your project.

advanced data-science machine-learning

For additional information on related topics, take a look at the following resources:

• Python AI: How to Build a Neural Network & Make Predictions (Tutorial)
• PyTorch vs TensorFlow for Your Python Deep Learning Project (Tutorial)
• Building a Neural Network & Making Predictions With Python AI (Course)