Rubix StudiosRubix StudiosDeep learning is a branch of machine learning that uses multi-layered neural networks to model and process complex patterns in data. Unlike traditional algorithms that rely on manual feature engineering, deep learning models automatically learn features from raw inputs such as images, audio, and text.
It powers many of today’s AI breakthroughs, from facial recognition and voice assistants to autonomous vehicles and advanced recommendation systems.
Advanced
Deep learning models are built from neural networks with many hidden layers, often requiring large datasets and high-performance computing resources. Techniques such as backpropagation, stochastic gradient descent, and regularisation are used to optimise training.
Specialised architectures include convolutional neural networks for image recognition, recurrent neural networks and transformers for sequential data, and generative adversarial networks for synthetic content creation. Large-scale deployments often rely on GPUs, TPUs, and distributed training across cloud platforms.
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Example
An autonomous driving system uses deep learning to process images from cameras and detect pedestrians, traffic lights, and lane markings. This allows the vehicle to make real-time driving decisions safely and accurately.