Gradient Descent: MNIST Dataset and Logistic Loss

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Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.