Deep Learning: Designing Neural Network Models

Related lectures (32)

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

Covers the fundamentals of multilayer neural networks and deep learning.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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

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

Explores neural network regularization, optimization, and practical implementation tips.

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.