Following are a few resources to help understand Artifical Neural Nets / Deep Learning.

• Brilliant intro to ANN. Noteworthy: Universal Approximation Theorem
• Brilliant intro to Backpropagation
• Russel and Norvig's Artificial Intelligence, A Modern Approach, 4th Global Edition
• Lectures by Florian Marquardt: Machine learning for physicists
• CS231n.stanford.edu
• Layer and Batch normalization
• Batch vs Online learning
• Types of learning
  • Zero-Shot
  • Self-supervised
• Reflections on scaling
  • Prediction: Human-equivalent neural networks by 2030
  • The Scale of the Brain vs Machine Learning
• End-to-end learning, the (almost) every purpose ML method