Neural Networks: Regularization & Optimization

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

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

Covers the design and optimization of neural network models in deep learning.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Covers the fundamentals of multilayer neural networks and deep learning.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

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

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

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.