Caffe Deep Learning Framework vs. C5i Discovery

Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.

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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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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

• Caffe is good for traditional image-based CNN as this was its original purpose.

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

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• Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
• Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
• Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
• The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.

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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

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

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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

TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.

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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

• Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.

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

• 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