matlab and python + numpy + scipy + matplotlib give the same kind of performance for the same level of features and ability to do quick exploratory prototyping with high level, vector based datastructures.
However one is free (beer and freedom) and multipurpose (you can preprocess string data and expose your model with a HTTP API) while the other is much less so.
In python + numpy you should always profile and if the bottleneck is in python interpreted code (rather than a low level numpy call) you can always rewrite the offending python loop in cython.
However one is free (beer and freedom) and multipurpose (you can preprocess string data and expose your model with a HTTP API) while the other is much less so.
In python + numpy you should always profile and if the bottleneck is in python interpreted code (rather than a low level numpy call) you can always rewrite the offending python loop in cython.