Artificial intelligence, machine learning, and deep learning; introduction to neural networks, data representations and tensor operations; gradient-based optimization and automatic differentiation; deep neural-network components; binary and multiclass classification; regression; model evaluation and data preprocessing; overfitting, underfitting, and regularization; convolutional neural networks; pretrained networks and transfer learning; and a reproducible end-to-end deep-learning workflow.
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