Convolutional Neural Networks: Fundamentals

Related lectures (32)

Page 2 of 4

Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

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

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

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

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

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

Covers the fundamentals of multilayer neural networks and deep learning.