
Explore neural network fundamentals, including architecture, training, and optimization techniques for efficient machine learning model development.

Explore loss functions and optimizers for quantum machine learning libraries. Learn to choose optimal parameters for accurate predictions and efficient model convergence in QML applications.

Explore neural collapse in deep classifiers, examining its impact on model behavior and performance. Gain insights into this phenomenon's role in understanding and improving deep learning systems.