Quantum Many-body Dynamics with Artificial Neural Networks

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

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

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

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

Explores environmental legislation, pollution control, noise regulations, and construction impact on the environment.

Explores weight sharing, filters, channels, and data augmentation in convolutional neural networks.

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

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

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