This lecture covers the concept of transfer learning with convolutional neural networks, explaining how to reuse pre-trained models for new tasks, the process of fine-tuning, and the impact of network depth and size on performance.
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Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.