Generalization in Deep Learning

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Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

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

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Explores the control of complexity in hypothesis spaces and the trade-off between approximation and estimation in risk decomposition.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.