Here are some reason to prefer deep learning:
- Deep learning models reduces the need for feature engineering and data preprocessing. This is particularly true in computer vision and natural language–related domains.
- Deep learning models are more robust in the presence of noise. Deep learning models can adapt to unique problems and are less affected by messy data.
- In some cases, deep learning delivers higher accuracy than other techniques for problems, particularly when data from a variety of sources must be used to address a problem.
- deep learning is powerful when we have access to lots of training data or when we have many dimensions or features of the data (time consuming feature extraction), for example
images, video, and audio.
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