Deep learning is a form of machine learning that uses artificial neural networks to analyse complex data structures. Inspired by certain aspects of how the human brain processes information, this method enables computer systems to learn patterns from large volumes of data and perform tasks with a high degree of accuracy.
Deep learning forms the foundation of many artificial intelligence systems. It operates through multilayered artificial neural networks, in which each layer processes data at a different level of abstraction. These layers work together to identify important features, recognise patterns and generate predictions or decisions. The term “deep learning” refers to the use of multiple processing layers within the neural network.
Compared with many traditional machine learning methods, deep learning can automatically learn relevant features from raw data with less manual feature engineering. During training, the model adjusts its internal parameters according to the data it receives and the errors identified in its outputs. However, human involvement is still required to select data, design the model, define objectives and evaluate performance.
Examples of deep learning applications include autonomous vehicles, security and surveillance systems, image recognition tools and virtual assistants such as Google Assistant.
The logical structure used in deep learning is built through artificial neural networks. These networks process and classify data by passing information through interconnected layers of artificial neurons. During training, the system identifies patterns, adjusts the strength of connections between neurons and improves its predictions over time.
Deep learning has a wide range of applications, including:
Image and Speech Recognition: Deep learning can identify and classify visual and audio data. This enables artificial intelligence systems to recognise objects, faces, voices and spoken language.
Natural Language Processing: Deep learning models can analyse text, interpret linguistic structures and extract meaning from written language. Text-based conversational systems such as ChatGPT are examples of technologies that use deep learning within natural language processing.
Autonomous Driving Technologies: Deep learning can be used to develop intelligent vehicle systems. Data collected from cameras, radar, lidar and other sensors can help vehicles identify objects, understand road conditions and support safer driving decisions.
