Training Neural Networks - Part II - Lecture 11

Delve into the intricacies of training neural networks in this comprehensive lecture from the University of Central Florida's Computer Vision course. Explore essential concepts such as loss functions, learning rates, momentum, and mini-batch training. Gain valuable insights into overfitting and learn effective strategies to mitigate it, including regularization techniques and dropout. This in-depth session, part of a broader curriculum covering mathematical preliminaries, image processing, deep learning, and various computer vision tasks, equips students with advanced knowledge in neural network optimization for improved performance in AI and machine learning applications.

Intro
Loss Function
Learning Rate
Loss
Momentum
Learning
MiniMatch Training
Insights
Overfitting
Avoiding Overfitting
Regularization Regulation
Dropout