Keras vs. Twilio

Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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I have tried other competitors. But I always end up going back to Twilio because I feel like it is dependable, easy to use, and, has competitive pricing. The only time I wouldn't recommend Twilio to someone as if they are completely inexperienced about using webhooks or APIs. Because you can't really just sign up and start using Twilio without connecting it to another platform first.

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• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• Easy to see my team in one place, when they log in and out and what call status they are in.
• Twilio is a simple, user friendly way to log in and out of work activities.
• Excellent services and support.
• The omnichannel feature which allows agents to get request from all channels.

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• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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• Control panel. Often, navigating through lists is challenging. I want endless scrolling, not pagination.
• Conversational intelligence service require a Language. I would like it to detect it.
• We would like to have some credit on clients that chose to use their own Twilio account.

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Unless we can get this handled quickly -- less than 1 week -- we will likely switch to another provider who, in my opinion, we'll have to spend close to $3,000 in development time to build a new integration for texting. Our clients need texting and I feel Twilio has failed us miserably.

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I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

Twilio has well documented APIs and examples. There are several tutorials, videos and Q&As regarding their services. So, usability is very good. I must say that advanced knowledge of telephony, API/Programming and error-handling is essential to make good use of Twilio. It's not just plug-and-play unless you are integrated with a system that has all of the programming built for it.

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Twilio executes what it is designed to do: send SMS messages at scale while providing very good deliverability. I believe that Twilio is very good at what we use for adding SMS messages to our comms strategy. We can see those messages get opened and replied to, which is exactly what we are looking to achieve.

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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

I have not had to communicate with Twilio support in the last 3 years but my past experience with them has been very positive. They replied to my previous requests promptly and kept me well informed to resolve my inquiries. With their documentation that's available, I hardly imagine why anyone would need to contact support since it's all there in a concise and easy to understand format. It would probably take you longer to type out a support ticket than to just open their doc websites.

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The developer APIs and SDKs are super easy to follow.

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Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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We evaluated many fundraising-based text-to-give programs and found the subscriptions prohibitively expensive for our small scale and uncertain first few years of development. While we may be willing to invest that kind of money after discovering how things work, we're happy with Twilio now and have no desire to start over.

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Being in South Africa, direct local currency support would be nice

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• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

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• We found that associates who had the opportunity to respond to a survey about their assigned job were 37% more likely to return to a job site
• We were able to reduce the number of resources required to manually respond to associates using Twilio Studio, so those resources could have more time to complete other tasks
• We were able to scale the number of associates who received survey messages at least by a factor of 8 without increasing our resource demand

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