Neural Networks Optimization

Related lectures (32)

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

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

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

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

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

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

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

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.