Visual Note-Making

A DevOps Engineer's Approach to Deep Learning

Today, I want to share my process of creating visual notes while studying artificial intelligence concepts. Even though I'm not naturally a visual person, I've discovered that incorporating visual elements into my note-taking system has tremendously improved my learning process. Let me walk you through my approach.

The Power of Visual Learning

While processing notes from "Artificial Intelligence: A Guide for Thinking Humans," I've developed a method that transforms complex concepts into visual mind maps. This approach has proven invaluable for someone like me who doesn't naturally think in pictures.

Here's why this matters: As engineers, we often deal with abstract concepts and complex systems. Visual note-making helps bridge the gap between theoretical understanding and practical application.

My Visual Note-Making Process

1. Start with a Central Concept

I begin with a central node - in this case, "AI Definitions." This becomes the anchor point from which all other concepts branch out. It's crucial to:

• Choose a clear, focused central topic

• Leave room for expansion

• Think about potential connections

2. Branch Out Systematically

From the central node, I create branches for key concepts. For example:

• Definition (including origin and key characteristics)

• Types of AI (Symbolic and Subsymbolic)

• Related concepts (Machine Learning, Intelligence, Learning)

3. Incorporate Visual Elements

Even though I'm not an artist, I use several techniques to make my notes visually engaging:

• Color coding for different branches

• Embedded images from my notes

• Related visual metaphors (like using album artwork to remember concepts)

• Clear hierarchical structures

Real-World Example: Breaking Down AI Concepts

Let me share how I applied this to understanding AI fundamentals:

1. Started with Definitions: Created a clear visual hierarchy showing AI's origin (McCarthy, 1956) and core definition

2. Added Types: Branched into Symbolic and Subsymbolic AI, showing their relationships

3. Connected Concepts: Linked related ideas like perceptrons and biological inspiration

4. Visual Anchors: Used meaningful images to cement concepts in memory (like connecting Subsymbolic AI to neural networks through neuron imagery)

Why This Method Works

This approach is powerful because:

1. Active Engagement: You're not just reading; you're actively processing and reorganizing information

2. Knowledge Gaps: The visual format quickly reveals areas where your understanding needs work

3. Flexible Growth: The mind map can evolve as your knowledge deepens

4. Natural Connections: Visual relationships help uncover connections between concepts

Pro Tips for Implementation

1. Don't Aim for Perfection: The goal isn't beautiful artwork; it's effective learning

2. Leave Space: Your knowledge will grow, so leave room for expansion

3. Use Tools: I use Excalidraw, but any digital drawing tool will work

4. Iterate: Your first version won't be final - keep refining as you learn

Making It Work for You

Whether you're studying cloud architecture, Kubernetes, or any complex topic, this visual approach can help you:

• Break down complex systems

• Identify relationships between components

• Remember key concepts more effectively

• Share knowledge with others

Looking Forward

This process has helped me transition from a nurse to a successful DevOps engineer. It's not just about making pretty diagrams - it's about developing a deeper understanding of complex topics through visual thinking.

If you want to learn more about these techniques and connect with other engineers passionate about learning, join our community at KubeCraft. We regularly discuss learning strategies, share resources, and help each other grow.

Keep learning, keep visualizing, and keep growing!

Mischa

P.S. How do you organize complex technical concepts? I'd love to hear your approaches!