DL and its recent advances
DL is a subset of ML that has gained significant attention in recent years due to its state-of-the-art performance on many complex tasks, such as image recognition, machine translation, and speech recognition. While traditional ML algorithms are limited by their inability to learn from vast amounts of data, DL algorithms can identify complex patterns and relationships within data by using deep neural networks, which consist of multiple layers of interconnected nodes that can learn to recognize increasingly abstract features.
The key difference between DL and traditional ML algorithms is the ability of deep neural networks to extract high-level features from raw input data. For example, in image recognition tasks, deep neural networks can learn to recognize edges, shapes, and textures in an image by processing the raw pixel data through a series of convolutional layers. Similarly, in NLP tasks, deep neural networks can learn to generate meaningful representations...
