Designing and Developing for Google Glass: Thinking by Allen Firstenberg, Jason Salas

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By Allen Firstenberg, Jason Salas

Growing apps for Google Glass is extra concerned than just studying easy methods to navigate its undefined, APIs, and SDK. you furthermore mght desire the precise attitude. whereas this functional e-book supplies the knowledge and strategies you want to construct and install Glass functions, it additionally enables you to imagine for Glass via displaying you the way the platform works in, and impacts, its environment.

In 3 parts—Discover, layout, and Develop—Glass pioneers consultant you thru the Glass environment and exhibit what this wearable machine capability for clients, builders, and society as an entire. You’ll methods to create wealthy performance for a shopper expertise that’s substantially assorted than whatever at the moment available.

Learn the 5 Noble Truths of serious Glassware design
Understand the Glass surroundings and study why it’s different
Sidestep Glass’s societal matters on your projects
Learn how Glass adapts to the user’s international, instead of the wrong way around
Avoid terrible layout through opting for Glassware antipatterns
Build cloud providers with the Google replicate API
Use the Glass improvement package to strengthen shopper applications
Submit your venture for evaluate within the MyGlass listing

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Extra resources for Designing and Developing for Google Glass: Thinking Differently for a New Platform

Example text

The clatrix implementation, however, takes less than a millisecond on average, although the first call that's made usually takes 35 to 40 ms to load the native BLAS library. Of course, this value could be slightly different depending on the hardware it's calculated on. Nevertheless, clatrix is preferred when dealing with large matrices unless there's a valid reason, such as hardware incompatibilities or the avoidance of an additional dependency, to avoid its usage. Next, let's look at scalar multiplication, which invloves simply multiplying a single value N or a scalar with a matrix.

By the term norm, we mean a function that has only positive values in the N-dimensional space. Let's visualize how the Euclidian space of the cost function of a formulated model varies with respect to the parameters of the model. For this, let's assume that the H parameter that represents the constant error is zero. A plot of the cost function J ( β ) of the linear model over the parameter β will ideally appear as a parabolic curve, similar to the following plot: [ 50 ] Chapter 2 For a single parameter, β , we can plot the preceding chart, which has two dimensions.

We can calculate this value by squaring the differences of all the given values of the dependent variable and their corresponding predicted values on the formulated linear model, and calculating the mean of these squared errors. The MSE is also termed as the mean-squared prediction error of a model. If the MSE of a formulated model is zero, then we can say that the model fits the given data perfectly. Of course, this is practically impossible for real data, although we could find a set of values that produce an MSE of zero in theory.

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