TensorFlow provides Insights into both data and machine learning strategies. The R&D with TensorFlow gives me that.

TensorFlow is an efficient product for building neural networks. 

The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market.

TensorFlow is easy to implement and offers inbuilt functions for various tasks. It supports image classification, object detection, and OCR. The availability of an app makes it simple to integrate OCR functionality directly to mobile applications.

I can find websites with a lot of open-source codes and this is the use case for me as well.

It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it. 

The most valuable feature for TensorFlow is the ability to use CoLab. It's actually also using Torch, but in TensorFlow according to my experience, it's much, much easier to do than the integration with Google CoLab. It's pretty simple to use Google CoLab Pro and use TensorFlow models. It's not a feature, but the best thing about TensorFlow and Keras is that it is the most common in the world and they have huge communities. Whatever error you have, you can actually Google that error and you can get it done in five minutes. So that is, I think, really unique about TensorFlow. I never actually thought about developing a system like TensorFlow. It's so huge and it needs a lot of developers to maintain, but if I want to develop a sub-system that actually helps me to solve a task, I can do that in just two days to develop benchmark models in TensorFlow. If I had to develop this from scratch I would probably need 20 days to a month to develop it myself from scratch. It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, improved the lives of many people around the globe. If it was a licensed product, a lot of people, for example, in the Middle East or the third world countries, would not be able to help their own communities because of a substantial license fee they cannot afford. The biggest lesson I learned is to have an open-source platform that could impact the world and make it a better place. You get that with TensorFlow.

Before version 2.X, PyTorch had features that were better than this product. Now that it's been updated, it's got all of those missing features and is much better. There's a significant difference.

Users are able to create deployments with Docker and TensorFlow. TensorFlow has a pre-trained model hub. It's a huge hub in a typical NLP or computer vision.

I've used TensorFlow in different areas within marketing tasks. For example, dynamic pricing solutions or classifications as to who will buy something or who will not buy something, or who will return. It's great to use in stock market scenarios, cryptocurrencies, foreign exchange markets, etc.

Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training. 

Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers.

It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models. 

The solution is quite useful for production. It tends to provide for digital devices or mobile devices. You can deploy your model on Android or iOS. I did that before on Android. It provides TensorFlow GS or JavaScript to run TensorFlow applications in the browser. 

It's quite a valuable solution when we go to production.

Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful. 

The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing.

TensorFlow is like a library. PyTorch is also a library. These are deep learning libraries that provide a set of functions. Ultimately you have to build a framework. TensorFlow as a whole is useful to us because we use a lot of functions, like activation functions or volition functions, feature mapping, and feature extraction. 

The primary feature that I personally like is the fact that TensorFlow allows us to utilize GPUs. At present, in data-driven deep learning, the most important thing is the usage of GPUs which accelerate the training of the model by many folds. What I love about TensorFlow is that it allows me to use GPUs.

TensorFlow is a framework that makes it really easy (and quick) to use deep learning. For example, it has an API, which is called 'Sequential API' , and using that, you can create a whole Deep learning model in about five lines of code. That's another core benefit from my perspective.

Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment. Especially the part where you could train the model again, then evaluate it if it's better than the previous versions. It will do the deployment on its own. The end-users will not really see the change, as the update takes place without any downtime.

It's got quite a big community, which is useful.