Module 3: Deep Learning & Neural Networks - The Technology Behind the AI Revolution

What is actually inside a neural network, and why did this become the dominant approach?

Deep learning powers most of what is called AI in 2026, from image recognition to large language models. The headlines treat neural networks as a black box. They are not. This module opens the box at the level a non-technical executive can use, without getting lost in calculus, so you can read the inside of any AI vendor's architecture diagram and tell a meaningful claim from a marketing one.

What you'll learn in this module

• What a neuron, a layer, and a parameter actually are, and why models with billions of parameters now dominate the field
• How training, backpropagation, and gradient descent work at the level that lets you evaluate compute and data requirements
• Why GPUs matter, why specific GPU generations matter, and what the hardware bottleneck means for vendor pricing
• The architectures that matter in 2026 (transformers, convolutional networks, diffusion models) and what each is and is not good for
• How to read research and vendor benchmarks without being misled by hand-picked datasets

The complete module gives you the literacy to challenge any deep-learning claim, vendor benchmark, or AI infrastructure budget with structured questions that actually surface the truth.