Artificial neural network

Related lectures (31)

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

Explores neural quantum states and their representation using artificial neural networks.

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

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

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

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

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

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