Deep learning is a subset of machine learning that utilizes multilayered neural networks, known as deep neural networks, to simulate the complex decision-making capabilities of the human brain. These networks consist of multiple layers of interconnected nodes (neurons) that process data hierarchically, enabling the system to learn intricate patterns and representations from vast amounts of data.

While both deep learning and traditional machine learning aim to enable machines to learn from data, they differ in approach:

‍Feature Extraction: Traditional machine learning often requires manual feature extraction, whereas deep learning models automatically learn features from raw data.

‍Data Requirements: Deep learning models typically require large datasets to perform effectively, while traditional models can work with smaller datasets.

‍Performance: For complex tasks like image and speech recognition, deep learning models generally outperform traditional machine learning models

Deep learning stands at the forefront of artificial intelligence advancements, offering unparalleled capabilities in processing and interpreting complex data. As technology continues to evolve, deep learning will undoubtedly play a pivotal role in shaping the future across various domains.