Difference Between Deep Learning and Traditional Machine Learning

1. Definition

Traditional Machine Learning (ML): A subset of AI that enables computers to learn from data and make predictions using structured algorithms like decision trees, support vector machines (SVM), and linear regression.
Deep Learning (DL): A specialized form of ML that uses neural networks with multiple layers (deep neural networks) to automatically extract patterns from large and complex datasets.

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2. Feature Engineering

ML: Requires manual feature selection, where data scientists define relevant features for training the model.
DL: Automatically extracts features from raw data, reducing the need for manual intervention.

3. Data Dependency

ML: Works well with small to medium-sized datasets.
DL: Requires massive datasets for optimal performance due to the complexity of deep neural networks.

4. Algorithm Complexity

ML: Uses simpler algorithms like logistic regression, decision trees, and random forests.
DL: Employs complex architectures such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.

5. Computational Power

ML: Requires moderate computational resources and can run on CPUs.
DL: Needs high-performance GPUs and TPUs due to the intensive matrix computations involved in deep networks.

6. Interpretability

ML: Easier to interpret and explain, making it suitable for applications requiring transparency (e.g., healthcare, finance).
DL: Acts as a “black box” with complex layers, making it difficult to understand how decisions are made.

7. Applications

ML: Used in fraud detection, customer segmentation, and recommendation systems.
DL: Applied in advanced fields like self-driving cars, natural language processing (NLP), and medical image diagnosis.

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