In this episode, we break down the core mechanics of neural networks—from how a single neuron processes information to how backpropagation enables large-scale learning. We explain weights, biases, and nonlinear activations, why depth gives networks their power, and how vanishing gradients once prevented deep learning from progressing. The discussion walks through loss functions, gradient descent, optimizers like Adam, and training stabilizers such as batch normalization and dropout. We close by examining biological limits of backpropagation and why adversarial examples reveal structural weaknesses in modern AI systems.

This episode covers:

• How neurons combine weighted inputs, bias, and nonlinear activation

• Why deep architectures learn hierarchical features

• Vanishing gradients and the rise of ReLU

• How backpr ...