Machine Learning and Deep Learning are subsets of Artificial Intelligence allowing computers to learn from data. However, they differ in several aspects.
Machine learning algorithms use structured, labeled data to make predictions. The input data for a model is a table with rows representing observations and columns representing attributes or features. This does not mean that they do not use unstructured data, but if they do, they must first go through a pre-processing task to organize them into a structured format.
Deep learning algorithms, on the other hand, can handle unstructured data, such as images, audio, and text. They automatically learn the most relevant features or representations from the raw data, eliminating the need for manual feature extraction. That allows deep learning models to capture complex patterns and relationships in the data that might be difficult for a human expert to identify.
Traditional
machine learning algorithms have relatively simple model architectures. Examples include logistic regression, decision trees, k-nearest neighbors, and support vector machines. These models generally have a fixed number of parameters and a predefined structure.
Deep learning models contain multiple layers of interconnected nodes called artificial neural networks. The depth of these models, represented by the number of layers, allows them to learn hierarchical representations of the data. As a result, deep learning models can be much more complex than machine learning ones, with tons of parameters to optimize.
Traditional
machine learning models require less computational power, and you can train them on standard CPUs. Many traditional algorithms are also more interpretable, making it easier to understand why they make certain decisions.
Training deep learning models can take a long time, depending on the dataset's size and the model's complexity. Deep learning models, especially those with many layers and a large number of parameters, require significant computational resources, often in the form of powerful GPUs, to train effectively. Additionally, you can consider deep learning models as "black boxes" due to their complexity, making it difficult to understand why they make specific decisions.
Machine learning algorithms perform well for many tasks, especially those with small to medium-sized datasets and relatively straightforward relationships between features. You can use them for classification, regression, clustering, and dimensionality reduction.
Deep learning algorithms excel when dealing with large datasets and complex tasks, such as image and speech recognition, natural language processing, and game playing. They often outperform traditional machine learning algorithms in these areas, as they can automatically learn and extract relevant features from the raw data.
In summary, the main differences between traditional machine learning and deep learning are the way they handle data representation, model architecture, computation, training, and their performance in specific use cases.
While deep learning has demonstrated impressive results in many complex tasks, choosing the appropriate approach based on the problem, available data, and computational resources is vital.
