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Covers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.
Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
