Maple vs. Pytorch

Maple is very useful in following scenarios :

• When doing some equation modelling for differential equations
• Writing code to simulate a electrical of mathematical system
• Speech and Image Processing Use case
• Linear Algebra Problems
• Lot of Options to plot 2-D and 3-D, including implicit, contour, complex, polar, vector field, conformal, density, ODE, PDE, and, statistical plot
• Engineering plots, including time and frequency domain responses and root-locus and root-contour plots

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They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

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• Numerical Calculation and Calculus
• Visualization of statistical and numerical data
• Predefined code templates for standard problem like finding FFT

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• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

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• On the software side and the software is bulky
• Code Editor can be improved
• Sometime software fails and hang when doing high calculation

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• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

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The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

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Maple is a very niche product and competes directly with Mathematica and MATLAB. It is a little expensive as compared to the other two however has more set of functions and libraries which makes it for suitable for high level and complex mathematics. It's editor is not on par with other two however it's visualization module is very good and powerful.

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Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

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• It saves time and does lot of hard calculation which is not possible manually
• Software is little bit expensive however has more functions as compared to Matlab and Mathematica
• Learning curve is little steep and hence take additional time to master

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• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info