expert.ai NL Suite vs. Pytorch

Existing body of data regarding our demographic targets, including social media, so for our psychographic targets, quite helpful. Alerts assist us in anticipating specific market demands. Yet AI is in its infancy so Cogito can only go as far as beta can go.

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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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• Humongous amounts of data can be found
• Autonomous
• Gives us actionable data and market insights and ideas

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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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• 500k bits of data is a lot.
• Visualization is good but there is room for more refinement.
• Harnesses AI, yet more potential there.

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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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Fast with just a few clicks and quite breathtaking what it finds. Beware you cannot search, only find!

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We were primitive using Salesforce, and while it is great, Cogito is a laser.

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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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• Identified new psychographic and demographic targets.
• Kept us in the tide of social media as it breaks.
• Improved our bottom line at least 7% so far.

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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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Independently Invited by TrustRadius

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