🧠 Deep Learning vs Machine Learning: What's the Difference?

People often use "Machine Learning" and "Deep Learning" interchangeably, but they're not the same thing. Understanding the difference is crucial for your AI journey.

Machine Learning

ML algorithms learn from structured data using predefined features. You tell the model what to look for. Examples: Linear Regression, Random Forests, SVMs. Works great with tabular data.

Deep Learning

DL uses neural networks with many layers to automatically learn features from raw data. No need to hand-craft features. Examples: CNNs for images, RNNs for text, Transformers for everything.

Key Differences

• Data: ML needs less data, DL needs massive datasets
• Hardware: ML runs on CPUs, DL needs GPUs
• Interpretability: ML models are explainable, DL models are black boxes
• Performance: DL outperforms ML on complex tasks (images, language, speech)

When to Use What?

Start with ML for structured data problems. Move to DL when you're working with images, text, audio, or when you have large datasets and GPU access.