In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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Introduction: Nursing education traditionally teaches skill acquisition in isolated practice drills and guided by step-by-step protocols. While these approaches may seem to provide a solid foundation, they do not adequately bridge the gap between a controlled learning environment and the reality of nursing practice. The constraints-led approach (CLA) is an applied theory, which explains that skill acquisition is a process of adjusting to the characteristics of a situation, instead of reproducing isolated, “ideal” movements out of context. Given that CLA has gained recognition as an effective learning method in various fields, it is worth investigating how CLA can be implemented for skill acquisition in nursing education. Methods: To gain insight into student experiences of several CLA-exercises, an explorative qualitative design was used. Ten longitudinal focus groups with nursing students (n = 11) were performed to gain deeper understanding of students’ experiences with an education course in which several “CLA-exercises” were integrated. In addition, the teachers (n = 3) involved were interviewed after the course was completed. Results: The students experienced the education course as enjoyable, challenging and reality-based. Also, the exercises motivated students to keep practicing. The students further appreciated the room for autonomy and self-organization. The teachers expressed enthusiasm for CLA-inspired education, noting the benefits of varied methods and the need for expert feedback and well-working practice materials. Conclusion: Both students and teachers felt confident that the students who completed this course were ready to apply the learned skills under supervision in clinical practice.
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Author supplied: "This paper gives a linearised adjustment model for the affine, similarity and congruence transformations in 3D that is easily extendable with other parameters to describe deformations. The model considers all coordinates stochastic. Full positive semi-definite covariance matrices and correlation between epochs can be handled. The determination of transformation parameters between two or more coordinate sets, determined by geodetic monitoring measurements, can be handled as a least squares adjustment problem. It can be solved without linearisation of the functional model, if it concerns an affine, similarity or congruence transformation in one-, two- or three-dimensional space. If the functional model describes more than such a transformation, it is hardly ever possible to find a direct solution for the transformation parameters. Linearisation of the functional model and applying least squares formulas is then an appropriate mode of working. The adjustment model is given as a model of observation equations with constraints on the parameters. The starting point is the affine transformation, whose parameters are constrained to get the parameters of the similarity or congruence transformation. In this way the use of Euler angles is avoided. Because the model is linearised, iteration is necessary to get the final solution. In each iteration step approximate coordinates are necessary that fulfil the constraints. For the affine transformation it is easy to get approximate coordinates. For the similarity and congruence transformation the approximate coordinates have to comply to constraints. To achieve this, use is made of the singular value decomposition of the rotation matrix. To show the effectiveness of the proposed adjustment model total station measurements in two epochs of monitored buildings are analysed. Coordinate sets with full, rank deficient covariance matrices are determined from the measurements and adjusted with the proposed model. Testing the adjustment for deformations results in detection of the simulated deformations."
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