We love TensorFlow…
What is Tensorflow?
TensorFlow is an open-source software library for machine learning across a range of tasks. It is a symbolic math library and also used as a system for building and training to detect and decipher patterns and correlate machined learning across production at Google. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source lcocorrelationNovember 2015
Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm-based search results.
We offer a number of different AI business services for Database Data Mining and Search engine analysis and much more. Sorry, we don’t give much detail but most of the work we do in AI requires substantial NDA’s. Let us build a Deep Learning neural net for you.
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What is a Tensor? If you have to ask… (Warning very complicated, short simple answer below the fancy answer)
An nth-rank tensor in m-dimensional space is a mathematical object that has n indices and m^n components and obeys certain transformation rules. Each index of a tensor ranges over the number of dimensions of space . However, the dimension of the space is largely irrelevant in most tensor equations (with the notable exception of the contracted Kronecker delta). Tensors are generalizations of scalars (that have no indices), vectors (that have exactly one index), and matrices (that have exactly two indices) to an arbitrary number of indices.
Tensors provide a natural and concise mathematical framework for formulating and solving problems in areas of physics such as elasticity, fluid mechanics, and general relativity.
The notation for a tensor is similar to that of a matrix (i.e., A=(a_(ij))), except that a tensor a_(ijk…), a^(ijk…), a_i^(jk)…, etc., may have an arbitrary number of indices . In addition, a tensor with rank r+s may be of mixed type (r,s), consisting of r so-called “contravariant” (upper) indices and s “covariant” (lower) indices . Note that the positions of the slots in which contravariant and covariant indices are placed are significant so, for example, a_(munu)^lambda is distinct from a_mu^(nulambda).
Or another way to understand it if you’re into math, a tensor is what happens when you mix linear algebra ( a matrix) with Calculus. And then you throw statistic into the pie for good measure inside.
My way of looking at it is, “A Matrix within a Matrix to infinity.”
Who would have thought that our brain thinks this way….