Why “Deep” Learning?
Deep Learning significantly influences other AI techniques, driving advancements in areas such as image recognition, natural language processing, and autonomous systems. But why is deep learning at the pinnacle of AI, and how does it make other AI techniques appear outdated?
Before addressing this, let’s clarify what deep learning actually is. As the name suggests, ‘deep’ refers to the multiple layers of learning involved in the process.
As François Chollet (founder of Keras) said the appropriate name for it should be “layered representations learning” or “hierarchical representations learning”.
Deep Learning is based on artificial neural networks to model complex patterns in data. These networks are composed of layers of interconnected nodes (neurons), which learn hierarchical features from vast amounts of data. By adjusting the connections and weights between neurons, deep learning models can recognize intricate patterns in tasks like image, speech, and text processing.
Now answering, the above question.
Deep learning surpasses classical machine learning techniques by automating a crucial step: feature engineering. In traditional…
