TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
B. Vogel, D. Spikol, A. Kurti, and M. Milrad. Proceedings of the 2010 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, page 65--72. Washington, DC, USA, IEEE Computer Society, (2010)
K. Juuti, and J. Lavonen. NorDiNa, (2006)Construction of research based teaching sequences through Developmental research (Linsje, 1995), Educational reconstruction (Duit, Komorek & Wilbers, 1997), or Ingenierie Didactique (Artigue, 1994), can be considered very similar with design-based research. On the one hand, these approaches take into careful consideration students’ previous knowledge and emphasise basic scientific concepts and how they are related to the teaching sequence (Méhuet, 2004) and on another hand they aim to design the artefacts. For example, Andersson and Bach (2005) produced a teacher guide as an artefact describing the research-based sequence for teaching geometrical optics. However, these approaches focus on research-based design and the adoption of the innovations needs, for example, teachers’ in-service training.
(p 56).
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A. Newell, and H. Simon. Communications of the ACM, 19 (3):
113-126(March 1976)p. 116:
"The Physical Symbol System Hypothesis. A physical
symbol system has the necessary and sufficient
means for general intelligent action."
p. 120:
"Heuristic Search Hypothesis. The solutions to
problems are represented as symbol structures.
A physical symbol system exercises its intelligence
in problem solving by search--that is, by
generating and progressively modifying symbol
structures until it produces a solution structure."
p. 121:
"To state a problem is to designate (1) a test
for a class of symbol structures (solutions of the
problem), and (2) a generator of symbol structures
(potential solutions). To solve a problem is
to generate a structure, using (2), that satisfies
the test of (1).".
B. Mott, S. McQuiggan, S. Lee, S. Lee, and J. Lester. Proceedings of the Agent Based Systems for Human Learning Workshop at the 5th International Joint Conference on Autonomous Agents and Multiagent Systems (ABSHL-2006), Hakodate, Japan, (2006)