Neural Networks: Logic and Applications

Related lectures (31)

Page 3 of 4

Explores optimization and approximation in deep neural networks, including optimal control and numerical experiments.

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Covers the fundamentals of multilayer neural networks and deep learning.

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

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

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

Covers the perceptron model and backpropagation algorithm in neural networks.

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

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.