Deep Learning Foundations

Deep Learning builds on the idea of stacking layers of artificial neurons to learn complex representations. Its foundation includes concepts like gradient descent, backpropagation, activation functions, and overfitting.

Though the architectures have grown deeper and data larger, many breakthroughs still trace back to foundational ideas. Understanding vanishing gradients or convolutional filters isn't optional—it's how you debug models that "almost work."

To master deep learning, don't skip the basics—modern models are just deeper, wider echoes of the same core principles.