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Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge. - GitHub - d2l-ai/d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
A book for Sense Publisher's 'Technology Enhanced Learning' series
Editors: Yishay Mor (London Knowledge Lab), Steven Warburton (King's College London) and Niall Winters (London Knowledge Lab)
Series editors: Richard Noss & Mike Sharples
Designing effective educational technology and technology-based activities is a non-trivial problem. This is often because the two need to co-evolve and thus no designer has a fixed reference point. Ideally, techno-pedagogical design needs to be an interdisciplinary exploration where every agent in the system – the learner, teacher, educational designer, and policy maker – is driven to constantly experiment and redesign their practices. This creates an acute need to find effective ways of sharing design knowledge in education. Yet the current literature, whether academic or professional, does not adequately support such sharing. Two extremes dominate: anecdotal accounts of personal experience on one side, and abstract theory on the other. From the perspective of practitioners aspiring to perfect their craft, both are problematic as they require significant interpretive effort before they can be applied to a new situation. This interpretive gap means that practitioners have difficulty building on the success of others in a cumulative manner. The current literature (in particular academic texts) tends to refer to a specific disciplinary or paradigmatic source. What practitioners need is to be able to access all facets of the educational experience and choose those that work best for their context, be it epistemic, pedagogic, organizational, social, and/or technological.
This book offers a broader approach to Hybrid Learning Spaces, a term that has recently moved from the periphery to the center of educational practice.
Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press.
The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all.