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 backpropagation and gradient descent update model parameters

• Optimizers such as Adam and RMSProp

• Stabilization techniques: batch normalization and dropout

• Biological alternatives to backpropagation

• The fragility exposed by adversarial examples

This episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

All referenced materials and extended resources are available at:

https://adapticx.co.uk