Understanding Visual Cortex with Deep Learning Models
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Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
