Neural Networks and Deep Learning Explained

How do artificial neurons and layers combine to approximate complex functions? What role do loss functions, gradients, and optimization algorithms play in training neural networks? Why does increasing depth and width often improve performance, and where are the diminishing returns? What practical constraints (data, compute, energy) limit how large and complex networks can become?

How do convolutional, transformer, and other architectures differ in how they process images and text? What enables a single model to handle multiple modalities like images, audio, and language together? How do pretraining and fine-tuning allow a model to adapt from generic data to specific tasks? What new capabilities emerge when models can connect visual, textual, and other signals in one system?

What is overfitting, and how can we detect when a model is memorizing instead of learning? Why are neural networks vulnerable to small, carefully crafted adversarial perturbations? How do techniques like regularization, data augmentation, and ensemble methods improve robustness? What does it mean to make a model robust not only technically but also socially and ethically?