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Tensorflow tries to fit the free parameters (usually millions of parameters) of a function y=f(x). The fitting algorithm gets usually thousands or millions of e
by s-macke 8y ago
Tensorflow tries to fit the free parameters (usually millions of parameters) of a function y=f(x). The fitting algorithm gets usually thousands or millions of examples of how the output y for a given input x has to look like.
For example, x can be tens of thousands of images of cats and dogs, and y can be 1 for a dog and 0 for a cat.
The goal for the fitting algorithm is to find parameters that describe the concept of a cat and a dog so that it can
can generalize and categorize general images of cats and dogs. A bad fit would be if the network just memorized the example images.
- deleted 8y ago[deleted]
- galfarragem 8y agoIf I understand it correctly: - Tensorflow is the best tool to make the grunt work necessary to calibrate the parameters of fitting algorithms. - Models of fitting algorithms are human proposed and where the ML 'art' is. Seems to be kind of reverse engineering.
- gaius 8y agoTensorflow is the best tool It is one of many competing tools, which is “best” depends ironically on many variables. CNTK is a rival for example.
- halflings 8y agoAccurate, although in most cases, the art is not in choosing the algorithm (often a regular feed-forward, covolutional or recurrent neural network) but the hyperparameters (regularization, number of layers, units, etc.) and sometimes the way the data is fed (augment the dataset with distortions? sample negative examples randomly? etc.)