Explores gradient descent methods for training artificial neural networks, covering supervised learning, single-layer networks, and modern gradient descent rules.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.
