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Deep Learning, as described by NPDL, is mobilized by four elements that combine to form the new pedagogies. They are: Learning Partnerships, Learning Environments, Pedagogical Practices, and Leveraging Digital. Born 1940, Michael Fullan is a Canadian educational researcher and former dean of the Ontario Institute for Studies in Education (OISE ...
The blog articles are written pro bono by major educational writers who advocate for the paradigm shift to Deeper Learning as well as by a balance of school leaders, teachers, professional learning specialists and others who are incorporating deeper learning practices into their curricula, instruction, assessment and system change plans.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Ng researches primarily in machine learning, deep learning, machine perception, computer vision, and natural language processing; and is one of the world's most famous and influential computer scientists. [34] He's frequently won best paper awards at academic conferences and has had a huge impact on the field of AI, computer vision, and robotics.
Ian J. Goodfellow (born 1987 [1]) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.He is a research scientist at Google DeepMind, [2] was previously employed as a research scientist at Google Brain and director of machine learning at Apple, and has made several important contributions to the field of deep ...
Richard G. Baraniuk is the C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University, the founder and Director of the open education initiative OpenStax (formerly called Connexions), and the founder and Director of the learning science and education research infrastructure SafeInsights.
Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...
Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images) with less manual feature engineering than prior methods, enabling significant progress in several fields including computer vision and natural language ...