Deep learning vs machine learning
The main difference between deep learning and traditional machine learning
- is that deep learning algorithms are designed to learn hierarchical representations of data,
- whereas traditional machine learning algorithms rely on hand-designed features.
- Deep learning algorithms can handle more complex and varied data
- than traditional machine learning algorithms,
- and they have achieved state-of-the-art performance on a wide range of tasks.
Deep learning is a subfield of machine learning that
- focuses on building artificial neural networks with many layers to solve complex tasks
- such as image and speech recognition, natural language processing, and autonomous driving
Deep learning algorithms are designed to automatically learn representations of data,
- without the need for manual feature engineering.
- The multiple layers in a deep learning model allow the algorithm to learn increasingly complex
- and abstract features of the data,
- starting with simple features like edges and shapes in the early layers
- and ending with high-level concepts like objects and scenes in the later layers.
Where as machine learning algorithms rely on hand-designed features
- and a limited number of layers to represent the data.
- These algorithms typically perform well on simpler tasks,
- but they can struggle with more complex tasks
- where the data has a high degree of variability
- or where the features are not well understood.