Lectures related to Neural network software

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

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

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

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.