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Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.
Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.
Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
