Convolutional Neural Network (CNN)

In this video, Professor Khanh introduces convolutional neural networks (CNNs) as an interesting and common deep learning model, particularly useful for image recognition.

He explains how CNNs are assembled with convolutional layers, ReLU activation, and pooling layers to classify images, such as determining the type of a car. He also highlights the importance of CNN layers, including convolutional, pooling, normalization, and fully connected layers. There are various CNN architectures like LeNet, AlexNet, and ResNet, emphasizing their application and performance in different case studies.

He also discusses the use of pre-trained models and showcases an example of applying CNN in bioinformatics using PSSM profiles for protein sequence identification. The study demonstrates CNN’s superior performance compared to traditional machine learning techniques, achieving higher accuracy in cross-validation and independent tests.

Would you be able to explain 4 important layers in a CNN and how do they contribute to the network’s performance?