Deep learning definition, algorithms, models, applications & advantages Science online

Deep learning is also known as deep structured learning or hierarchical learning, It is part of a broader family of machine learning methods based on the layers used in artificial neural networks, Deep learning is a subset of the field of machine learning, which is a subfield of AI, Deep learning applications are used in industries from automated driving to medical devices. It is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation, Each successive layer uses the output from the previous layer as input, It can be learned in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners, It enables computational models which are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. It is a subfield of machine learning concerned with algorithms inspired by the structure & function of the brain called artificial neural networks, It can teach computers to do what comes naturally to humans: learn by example, Deep learning can be used in driverless cars, allowing them to recognize the stop sign, or to distinguish the pedestrian from the lamppost. The computer model learns to perform classification tasks from images, text, or sound, Deep learning models can achieve state of art accuracy, sometimes exceeding human-level performance, Models are trained by using a large set of labeled data & neural network architectures that have many layers. Neural networks are static & symbolic, They were inspired by information processing & distributed communication nodes in biological systems synaptic structures, they have many differences from the structural & functional properties of biological brains, that make them incompatible with the neurological evidence, while the biological brain of most living organisms is dynamic (plasticity) and analog.