Linked Notes
Machine & Deep Learning
The most important difference between traditional machine learning and deep learning, is how good deep learning is at working with unstructured data, such as images, audio, and text. Deep learning models excel at working with this type of data because they can automatically extract features from the raw data, which is something that traditional machine learning models would require manual feature engineering to do. This is one of the reasons why deep learning has become so popular in recent years, as it allows for more accurate and efficient models to be built with less human intervention.
For example, in image classification tasks, deep learning models can learn to recognize objects in images by automatically extracting features such as edges, textures, and shapes from the raw pixel data. In contrast, traditional machine learning models would require manual feature engineering to extract these features from the images before they could be used for classification.