How do deep learning and traditional machine learning differ? Get Best Data Analyst Certification Course by SLA Consultants India

Difference Between Deep Learning and Traditional Machine Learning

Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data and make predictions. Within ML, there are two main approaches: traditional machine learning and deep learning.

How do deep learning and traditional machine learning differ? Get Best Data Analyst Certification Course  by SLA Consultants India

1. Feature Engineering

• Traditional Machine Learning: Requires manual feature selection. Domain experts define which attributes (features) are most relevant for the model.
• Deep Learning: Automatically extracts features from raw data using neural networks, reducing the need for manual feature engineering.

2. Data Requirements

• Traditional ML: Works well with smaller datasets and structured data.
• Deep Learning: Requires large datasets and high computational power due to complex models.

3. Algorithms Used

• Traditional ML: Includes algorithms like Decision Trees, Support Vector Machines (SVM), Random Forest, and Logistic Regression.
• Deep Learning: Uses artificial neural networks (ANN), particularly deep neural networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).

4. Interpretability

• Traditional ML: More interpretable, allowing users to understand why a model made a decision.
• Deep Learning: Often considered a “black box” since decisions are made through complex neural network layers.

5. Processing Power

• Traditional ML: Requires less computational power and can run efficiently on standard processors.
• Deep Learning: Needs powerful GPUs or TPUs due to intensive matrix computations.

6. Application Areas

• Traditional ML: Used in fraud detection, credit scoring, and recommendation systems.
• Deep Learning: Applied in image recognition, speech processing, autonomous driving, and natural language processing (NLP).

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Conclusion

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