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What is Deep Learning?
Deep Learning is a type of machine learning that uses neural networks with many layers. It is especially good at tasks involving images, audio, and text, and it is behind most modern AI breakthroughs.
Deep Learning Explained Simply
Regular machine learning often requires human experts to select which features to use (like: use petal length and width for flower classification). This is called feature engineering.
Deep Learning removes this requirement. You give the algorithm raw data (like the actual pixels of an image), and it figures out which features matter on its own.
It does this by passing the data through many layers of processing, each layer learning increasingly complex representations:
- Layer 1: detects simple edges in an image
- Layer 2: combines edges into shapes
- Layer 3: combines shapes into object parts
- Layer 4: recognizes the whole object
The 'deep' in deep learning refers to these multiple layers.
Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
What Makes Deep Learning Different
- Traditional ML: humans select features, algorithm learns relationships between them
- Deep Learning: algorithm automatically learns features AND relationships from raw data
- Deep Learning needs much more data and computing power than traditional ML
- Deep Learning is often harder to interpret (we may not know why it makes a decision)
- Deep Learning currently achieves the best results on images, audio, and natural language
When to Use Deep Learning vs Regular ML
- Use Deep Learning: when working with images, audio, video, or large text datasets
- Use Deep Learning: when you have lots of data (usually at least tens of thousands of examples)
- Use regular ML: when you have a small dataset and can identify features yourself
- Use regular ML: when you need a simple, fast, and interpretable model
- Use regular ML: for tabular data (spreadsheets) with clear numerical features
Tip
Tip
Deep Learning does not replace regular machine learning. For many business problems with tabular data (spreadsheets), algorithms like Random Forest and Gradient Boosting outperform deep learning with far less complexity and data. Use the right tool for the right job.
Key Takeaways
- Deep Learning is a type of machine learning that uses neural networks with many layers.
- Traditional ML: humans select features, algorithm learns relationships between them
- Deep Learning: algorithm automatically learns features AND relationships from raw data
- Deep Learning needs much more data and computing power than traditional ML