Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.