MATLAB vs. Pytorch

MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.

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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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• It has a very user friendly library which helps users learn this software fairly quickly in a short span of time.
• The graphical user interface provided by the software is really good.
• The code that a person writes allows options for debugging.
• One can visualize the flow of control of their code inside MATLAB.

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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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• MatLab is pricier than most of its competitors and because of this reason, many organizations are moving towards cheaper alternatives - mostly Python.
• MatLab is inefficient when it comes to performing a large number of iterations. It gets laggy and often crashes. Python is better in this regard.
• There is a limited number of hardware options (mostly NI) that can be connected directly to the data acquisition toolbox.

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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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MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.

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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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The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.

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How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.

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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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• MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process.
• The software makes it super easy for us to create plots that we can then show to investors or clients to display our data.
• We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language.

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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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