MatConvNet is a MATLAB toolbox for implementing Convolutional Neural Networks (CNNs) in computer vision applications. It emphasizes simplicity and flexibility, providing easy-to-use MATLAB functions for building CNN components such as linear convolutions, feature pooling, and activation functions. MatConvNet supports both CPU and GPU computation, enabling efficient training of complex models on large datasets like ImageNet. The toolbox includes pre-trained models, wrappers for standard CNN architectures, and examples for learning on various datasets. It is designed to be user-friendly and efficient, making it suitable for both research and educational purposes. The documentation covers the computational aspects of neural networks, backpropagation for derivative computation, and detailed descriptions of the computational blocks in the toolbox.MatConvNet is a MATLAB toolbox for implementing Convolutional Neural Networks (CNNs) in computer vision applications. It emphasizes simplicity and flexibility, providing easy-to-use MATLAB functions for building CNN components such as linear convolutions, feature pooling, and activation functions. MatConvNet supports both CPU and GPU computation, enabling efficient training of complex models on large datasets like ImageNet. The toolbox includes pre-trained models, wrappers for standard CNN architectures, and examples for learning on various datasets. It is designed to be user-friendly and efficient, making it suitable for both research and educational purposes. The documentation covers the computational aspects of neural networks, backpropagation for derivative computation, and detailed descriptions of the computational blocks in the toolbox.