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Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.
Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.
