The Problem of Overfitting

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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

Covers data representation, MLP training, activation functions, and gradient-based learning in deep neural networks.

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

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

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.