C5i Discovery vs. Pytorch

Course5 Discovery was able to help my organization with cross-functional data by monetizing it. Our data was transformed into augmented dashboards which are easy to navigate - I've never used a platform like this, so I found that this solution minimal enough for a beginner, but also intelligent enough for a seasoned professional. Course5 Discovery was able to curate personal stories around our data which allowed us to make better business decisions because we were basing them on facts and analytics. More importantly, these analytics are presented in a way that makes sense! The insights provided to our business were a mix of facts that were both predictive and prescriptive, which brought out the business to the next level. We no longer have to take the time to run endless reports, and then spend hours trying to decipher them. Instead, with Course5 Discovery all the information, data, and insights we need are right in front of us when we need it most.

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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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• Data Visualization.
• Predictive Modeling.
• Trend Analysis.
• Data Analysis.

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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

• 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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• Multi-Platform use.
• Mobile Suport.
• Customization.

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Independently Invited by TrustRadius

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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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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

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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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

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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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

• We saved time because the analytics were presented in a way that made sense.
• Return on Investment.
• Scalability as a company.

Incentivized

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

• 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

Incentivized

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