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What is Deep Learning (DL)

Applications of Machine Learning and Artificial Intelligence in Education
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.
Published in Chapter:
Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE)
Mahbubur Rahman (North American University, USA) and Mustafa Duran (North American University, USA)
DOI: 10.4018/978-1-7998-7776-9.ch005
Abstract
The authors emphasize the application scope of the deep learning (DL) technologies in the instructional analysis, design, development, implementation, and evaluation of the educational tools that can enhance the 21st-century modes and models of learning and instruction. The latest trend in the remote learning systems opens a wonderful opportunity for the DL technologies to be integrated with these systems that can impact the way of learning, teaching, design, and development of such systems. The DL technologies provide the data driven decisions and analytical outcomes that can be integrated with the educational technologies. The existing educational technologies and remote learning systems lack in such integrated DL services that can impact the overall education learning systems of the 21st century. The learners and instructors can also benefit from such DL-integrated educational tools. The authors expand further details on the application and implication of the DL services mentioned above in the chapter.
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More Results
The Image as Big Data Toolkit: An Application Case Study in Image Analysis, Feature Recognition, and Data Visualization
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).
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Cloud Analytics: Introduction, Tools, Applications, Challenges, and Future Trends
Deep learning is a type of machine learning that uses artificial neural networks to learn from large amounts of data and solve complex problems.
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Deep Learning on Edge: Challenges and Trends
A class of machine learning algorithms for automation of predictive analytics.
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Machine Learning Applications for Vibration-Based Structural Health Monitoring
Subset of ML, that in essence is a deep Neural Network (NN) technology with many hidden layers. DL is supposed to imitate the complex way that humans gain certain kinds of knowledge.
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A Meta-Analytical Review of Deep Learning Prediction Models for Big Data
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.
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Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization
Is a branch of machine learning with a set of algorithms modelling high level abstractions in data using deep graphs having multiple processing layers.
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Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT)
It is a field of ML that uses a hidden neural network where two or more interrelated layers are present.
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Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations
Technology is a method in AI that teaches computers to process data in a way inspired by the human brain. DL models can recognize the complex picture, text, sounds, and other data patterns to produce accurate insights and predictions.
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Analysis of Ethical Development for Public Policies in the Acquisition of AI-Based Systems
Corresponds to a subset of artificial intelligence techniques that compromises models based on artificial neural networks.
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Deep Learning Approaches for Affective Computing in Text
It is a subfield of machine learning that uses artificial neural networks to learn from analyzed data.
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Harnessing the Power of Artificial Intelligence for Modelling and Understanding Cultural Heritage Data
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.
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Deep Learning Techniques and Optimization Strategies in Big Data Analytics: Automated Transfer Learning of Convolutional Neural Networks Using Enas Algorithm
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.
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Plant Disease Classification Using Deep Learning Techniques
DL is a subset of machine learning that uses artificial neural networks to model and solve complex problems by learning from large amounts of data.
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Fairness Challenges in Artificial Intelligence
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The Understanding of Spatial-Temporal Behaviors
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.
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Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems
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.
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Convolutional Neural Network
A class of machine learning algorithms for automation of predictive analytics.
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