Скачать или смотреть 4.CIFAR-10 Classification with Convolutional Neural Networks

sample code: https://github.com/mhe931/ml4cv
These materials provide a comprehensive overview of Convolutional Neural Networks (CNNs) and their practical application in computer vision. The lecture notes detail the historical evolution of network architectures, highlighting the transition from simple linear classifiers to complex models like ResNet and VGG. Key technical concepts such as Batch Normalization and Residual Blocks are explained as essential tools for stabilizing training and enabling deeper architectures. Complementing the theory, the exercise set guides students through a hands-on project using the CIFAR-10 dataset to build and refine a CNN. Students are instructed to implement advanced features like max pooling, regularization, and weight visualization to understand model performance. Ultimately, the sources combine academic foundations with technical instructions to teach the design and optimization of deep learning models.