How do deep learning models differ from traditional machine learning algorithms?

Deep learning and traditional machine learning are both subsets of artificial intelligence, but they differ significantly in how they process data and learn from it.

1. Feature Engineering

Traditional Machine Learning: Requires manual feature engineering, where domain experts identify the most relevant features for the model. Algorithms like decision trees, support vector machines (SVM), and random forests rely on well-structured data.
Deep Learning: Automatically extracts features using neural networks. It eliminates the need for manual feature selection, making it highly effective for complex data like images and text.

2. Data Dependency

Traditional ML: Works well with smaller datasets where structured patterns can be identified.
Deep Learning: Requires large datasets for effective training. Neural networks need vast amounts of labeled data to make accurate predictions.

3. Computational Power

Traditional ML: Can run on standard CPUs and requires less computational power.
Deep Learning: Needs high-performance GPUs and TPUs due to complex mathematical computations in deep neural networks.

4. Performance on Complex Data

Traditional ML: Works well with tabular data, structured databases, and numerical data.
Deep Learning: Excels in handling unstructured data like images, audio, and text using architectures such as CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks).

5. Interpretability

Traditional ML: Easier to interpret and explain decisions using feature importance and rule-based models.
Deep Learning: Functions as a “black box,” making it harder to interpret the reasoning behind predictions.
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