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I think the best explanation is the Pareto Principle, where in this case, 80% of the the performance comes from 20% of the code. Most machine learning frameworks have a Python API that developers use, but the internals are usually implemented using C++ or CUDA (for GPU acceleration) or using specialized libraries like BLAS. This latter component is the 20% ...


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I think it's because the actual computation isn't being done by Python, but with the optimized libraries (like tensorflow) that are built with low level programming languages. The only parts that Python is being used for is the basic program structure and getting the data into the high performance machine learning libraries. Also Python is more like a ...


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You probably got the back propagation wrong. I have done a test on the accuracy on adding an extra layer and the accuracy went up from 94% to 96% for me. See this for details: https://colab.research.google.com/drive/17kAJ2KJ36grG9sz-KW10fZCQW9i2Tf2c To run the notebook click Open in playground and run the code. There is a commented line which add 1 extra ...


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