Deep learning is chosen because it has several advantages over traditional machine learning algorithms:
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Better performance: Deep learning algorithms can achieve higher accuracy levels than traditional machine learning algorithms.
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Feature extraction: Deep learning algorithms are able to automatically extract relevant features from raw data, which simplifies the feature engineering process.
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Complex patterns: Deep learning algorithms can detect complex patterns in data that are difficult for traditional machine learning algorithms to identify.
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Scalability: Deep learning algorithms can scale to large datasets and parallel computing architectures.
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Unsupervised learning: Deep learning algorithms can learn from unlabeled data, which is useful in scenarios where labeled data is limited or expensive to obtain.
Overall, deep learning enables the creation of complex models that can accurately capture and analyze intricate relationships within large datasets, making it an effective tool for a wide range of applications.