# Neural Networks and Deep Learning Basics

> Master the building blocks of modern AI. Understand perceptrons, hidden layers, activation functions, and backpropagation from a security perspective.

Canonical: https://codelivly.com/rooms/neural-networks-and-deep-learning-basics
Last updated: 2026-09-29

| | |
|---|---|
| Level | Beginner |
| Category | Cybersecurity |
| Lessons | 4 |
| Estimated time | 30 min |
| XP | 100 |

This module demystifies the core architecture of neural networks and deep learning for cybersecurity professionals. You will explore how artificial neurons (perceptrons) work, how they connect to form hidden layers, and the critical role of activation functions in introducing non-linearity. Crucially, you will learn how models "learn" through forward propagation, loss calculation, and backpropagation. By understanding these mathematical foundations, you will be equipped to identify how adversarial attacks manipulate weights, gradients, and activation pathways to compromise AI systems.

## Lessons

1. What is a Neural Network (30 min)
2. Layers, Weights and Activation Functions Explained Simply (30 min)
3. What is Deep Learning and Why It Enabled Modern AI (30 min)
4. Guided Lab: Visualizing a Simple Neural Network's Decisions (30 min)

## Related

- [Learning path: AI and Machine Learning Fundamentals](https://codelivly.com/learning-paths/ai-and-machine-learning-fundamentals)
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