Caffe Deep Learning Framework vs. Qlik Sense

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.

Incentivized

Qlik Sense is a program whose purpose is to greatly improve all your operations and use of all data in an organic way. The mission will always be to increase the economic and commercial processes of the company in a short time. I recommended it for its high technology, which was Created for this area, the results are successful. We have noticed how it has increased relationships with our clients thanks to the credibility and security that we provide.

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

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• Flexibility in data recording, from the app, as well as in handling them. Allowing you to view reports quickly and effectively.
• Easy to generate dynamic graphics according to our needs.
• It is very easy to use, intuitive, so it does not generate costs in the learning process compared to the tool with collaborators

Incentivized

• 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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• Scalability is RAM limited, all data is associative and in-memory so server's RAM is directly proportional to performance
• Limitation on advanced analytics capabilities in the form of R integration to perform complex statistical and analytical calculations
• Can have more intuitive interface

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Qlik Sense is a constantly improving it's software and working with its' users to make it better. They are great at keeping their users informed of progress and care about delivering a quality product

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Standard user interface and powerful analytic functions is GREAT. As technical person working in the background there are more things to do to make this a completely great tool. Some functions that should be standard requires consult scripting and hours. Now we are using it quite advanced and with many servers and in combination with QlikView. So overall I love the tool. But it could be better and user friendly and powerful in the background

Incentivized

Not only can you ask the support team for help, but you can also ask the community. Also with the community there is a vast amount of problems that have already been solved. The problem you are encountering has a likely chance of already being discussed and even solved in the community section saving you time from reaching out.

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The instructor was very knowledgeable.

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The online instructor was very knowledgeable.

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Hire Qlik consultants. It's better done by them or with their aid.

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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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The customization of the platform opens up plenty of other options depending on the use cases. The API layer is incredibly rich and makes integration of Qlik based visualization into web pages a simple and effective pattern. It's been very easy to use with a great community made up of professionals. Qlik Sense has introduces artificial Intelligence into my data visualization and reporting activity.

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

Incentivized

• Once an app is published and running good, we can quickly see the time we save to analyze data.
• It's hard sometimes to realize the money we save by using this system.
• People start understanding the power of data and asking more questions.

Incentivized