Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.
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Neuton
Score9 out of 10
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Bell Integrator offers Neuton, an automated machine learning (Automated ML) application supplying AI learning and assistance to analytics and or business processes.
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.
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 machine learning modeling and time-series forecasting are the best things that Neuton's platform provides. Researchers in the field of healthcare, marketing and various other industries can use this platform to get more in-depth insights into the dataset that they have been working on. Neuton.ai is going to bring in image detection and text analysis in the future which makes the perfect choice for people from product management profiles and various Data Science backgrounds.
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'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.
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
User Onboarding with Google cloud platform is the most confusing part, this can be definitely be improved
UI of the platform
Front end of the website seems simple, little more features can be added so that people or users can navigate to various pages and know more about the platform
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.
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
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