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Delves into convolutional filters as an inductive bias for images in neural networks, emphasizing independence to translation and local feature detectors.
Explores the evolution of CNNs in image processing, covering classical and deep neural networks, training algorithms, backpropagation, non-linear steps, loss functions, and software frameworks.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
Introduces convolutional neural networks for image processing, covering basic components, architectures, and practical applications, including denoising and segmentation.