Deep learning offers several advantages over traditional machine learning approaches, including:
Ability to process large amounts of data: Deep learning models can analyze and process massive amounts of data quickly and accurately, making it ideal for tasks such as image recognition or natural language processing.
Automated feature extraction: In traditional machine learning, feature engineering is a crucial step in the model building process. Deep learning models, on the other hand, can automatically learn and extract features from the raw data, reducing the need for human intervention.
Better accuracy: Deep learning models have shown to achieve higher accuracy levels in complex tasks such as speech recognition and image classification when compared to traditional machine learning models.
Adaptability to new data: Deep learning models can adapt and learn from new data, making them suitable for use in dynamic and ever-changing environments.
While deep learning does have its advantages, it also has some limitations, such as requiring large amounts of data and computational resources, making it unsuitable for some applications.
