Deep Neural Network Solution of the Electronic Schrödinger Equation

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

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

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

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Explores convolutional neural networks, covering convolution, cross-correlation, max pooling, layer structure, and examples like LeNet5 and AlexNet.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

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

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.