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Interesting! I wrote in a draft post back in late 2013 [1], that asked: "What if one could have a fully declarative “matrix language” in which all data transfo
by samuell 11y ago
Interesting! I wrote in a draft post back in late 2013 [1], that asked:
"What if one could have a fully declarative “matrix language” in which all data transformations ever needed could be declaratively defined in a way that is very easy to comprehend?"
I'm now pondering whether TensorFlow isn't quite an answer to this question?
[1] Posted the draft now for reference: http://bionics.it/posts/matrix-transformation-as-model-for-data-flow-operations http://bionics.it/posts/matrix-transformation-as-model-for-d...
- davmre 11y agoThe idea of representing a program by a declarative computation graph of matrix transformations has been big in deep learning research for a few years. Theano [1] is the canonical example, though more recently the space has gotten bigger with other frameworks including CGT [2] and now TensorFlow. The computational graph abstraction is really nice in part because it gives you very straightforward automatic differentiation, which dovetails very nicely with the trend in machine learning of casting a wide range of algorithms as (stochastic) gradient ascent on some loss function. [1] http://deeplearning.net/software/theano/ http://deeplearning.net/software/theano/ [2] http://rll.berkeley.edu/cgt/ http://rll.berkeley.edu/cgt/
- samuell 11y agoThanks! Yea, though in fact my idea is more about wiring the actual (matrix/tensor) operation with dataflow, rather than just the dataflow between such operations. But it might be bit of a different problem area :)