What is Deep Learning (DL)

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The deep learning (e.g., DL) is the subset of the machine learning that allows machines to solve complex problems from the multidimensional, complex datasets. Some examples of the DL include self-driving cars, natural language processing, visual recognition, fraud detection, etc. Learn more in: Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE)

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A branch of machine learning based on learning based data representations and algorithms modeling high level data abstractions. Deep learning uses multiple, complex processing levels and multiple nonlinear transformations. For a review of deep learning techniques, please see Masters (2015) AU128: The in-text citation "Masters (2015)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. , and Awad and Khanna (2015). Learn more in: The Image as Big Data Toolkit: An Application Case Study in Image Analysis, Feature Recognition, and Data Visualization

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A class of machine learning algorithms for automation of predictive analytics. Learn more in: Deep Learning on Edge: Challenges and Trends

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Deep learning is subset of machine learning that is used to build learning models that can help to understand and analyze large data and support complex predictions for decision making. Learn more in: A Meta-Analytical Review of Deep Learning Prediction Models for Big Data

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Is a branch of machine learning with a set of algorithms modelling high level abstractions in data using deep graphs having multiple processing layers. Learn more in: Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization

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Corresponds to a subset of artificial intelligence techniques that compromises models based on artificial neural networks. Learn more in: Analysis of Ethical Development for Public Policies in the Acquisition of AI-Based Systems

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is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Learning can be supervised, semi-supervised or unsupervised. Learn more in: Harnessing the Power of Artificial Intelligence for Modelling and Understanding Cultural Heritage Data

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A part of machine learning with its algorithms, to the structure and working of the brain called artificial neural networks; a knowledge process and a way to automate Predictive Analytics. Learn more in: Deep Learning Techniques and Optimization Strategies in Big Data Analytics: Automated Transfer Learning of Convolutional Neural Networks Using Enas Algorithm

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DL is also ML relying on DNN. Learn more in: Fairness Challenges in Artificial Intelligence

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A new area of machine learning research. It uses a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. It also learns multiple levels of representations that correspond to different levels of abstraction. Deep learning algorithms are based on distributed representations. The composition of a layer of nonlinear processing units used in a deep learning algorithm depends on the problem to be solved. Learn more in: The Understanding of Spatial-Temporal Behaviors

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Deep learning is a sub-branch of machine learning theory. It allows us to train an agent model to predict outputs with a given dataset. Both supervised and unsupervised learning can be used for deep learning models. Learn more in: Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

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A class of machine learning algorithms for automation of predictive analytics. Learn more in: Convolutional Neural Network