Principles of Self-Organization

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Page 2 of 4

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

Explores using deep learning models to understand the visual cortex and sensory portraits.

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

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.

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

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

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

Explores data representations, histograms, neural networks, and deep learning concepts.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.