TensorFlow Reviews

I am using TensorFlow for many computing projects, such as image data analysis, data compressions, and data processing.

I have used TensorFlow GS, TensorFlow for mobile, and TensorFlow Lite. The way things operate is changing from a server-side to edge computing and mobile devices. I have been working on how to reduce the resource usage, we have downgraded and should have good performance with the best accuracy in our machine and models.

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

There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access.

There should be better integration and standardization with different operating systems. We need to always convert from one model to another and there is not a single standardized model output that we could use on different platforms, such as Intel x56, x64 based, AR-based, or Apple M1 chips.

I have been using this solution within the last 12 months.

TensorFlow is a reliable solution, but we have not explored all the aspects of TensorFlow. We have been building our customized applications, such as libraries, features, or functions. We only use the features that allow our application to work. Different areas need to be researched on a low level to make them more efficient.

We have been working on specific applications and any model built on TensorFlow can be applied to any scalable level. The solution has built-in scalability.

I have not needed to use technical support.

I did not require a license for this solution. It a free open-source solution.

I rate TensorFlow a ten out of ten.

I primarily used the solution for computer vision applications, for example, detection and segmentation, and OCR.

We used an architecture from a published paper. It was based on TensorFlow and we upgraded it and developed on it. I also worked on face verification and likeness detection. We are working on anti-spoofing detection. We did some things around face verification and likeness detection. I used TensorFlow specifically.

I've also used the solution to detect hands, tracking customers in the supermarkets, and using the solution for detecting the pickup and dropping of objects from shelf to basket in the supermarket.

Lately, I've been working on a project in Arabic that is designed to detect handwriting. I also use PyTorch to help with this task.

For one particular project, we did an extraction of the Arabic language from a crucial document, like an ID. We needed to capture the ID using the application so that the application sends the ID to the server. We needed to make an Egyptian ID detection on mobile. I built a simple commercial network to customize the ID and converted it into TensorFlow White and made some compositions to make it faster to run. We deployed it on the mobile. For this bot, there's full support in this area, which is great.

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. 

Overall, the solution has been quite helpful. I can't recall missing any features when I was using it.

I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great.

The solution is quite stable. It's reliable. It doesn't crash or freeze. There are no bugs or glitches to deal with. We've been happy with the performance.

The scalability of the solution is good. When it comes to TensorFlow (or PyTorch) you can train on multiple GPUs at the same time, and multiple machines at the same time, also. 

In TensorFlow, I didn't train on multiple GPUs, yet, however, I know it's very easy and straightforward. I've been through it. Scaling should be a problem for a company. If they want to scale, they can.

I've never been in touch with technical support. I can't speak to their level of knowledge or how quickly they respond.

I also use PyTorch and Amazon SageMaker.

Amazon Solution provides the how-to where we can use PyTorch and TensorFlow to train models on huge datasets on AWS end-user. They are complementing each other on a project.

The entire implementation process is quite straightforward. It's not complex at all.

The deployment is very fast. You can have it up and running in five minutes. You just go and install the version you would like to use and you are done. It's all very simple.

I always go with the deployment with TensorFlow. I don't try to use PyTorch in this area. A year ago, when I was deploying a semantic segmentation model on the server, each time I sent a request that I need to reload the model input that ends.

It's my understanding that the version we use is free. It doesn't cost any money.

When we did the utilization applications, we were deploying on digital ocean servers. For the projects that I'm working on now, we are planning to deploy it on its own port attached to the robot. We haven't done it, yet. We are finishing the project right now. For deploying the solutions, I deploy them on the digital ocean. 

I'd recommend the solution. I'd also recommend users considering the solution do a bit of studying. There are some great courses on Coursera and there's a recent one called DeepLearning.AI that is extremely useful.

Overall, as I use the product pretty much everywhere, I would rate it at a ten out of ten.

I have experience in NRP and time series forecasting and also in marketing-relevant tasks.

For example, I've used the workaround cutoffs to create a deep learning network to classify binary classification. I've done binary classification tasks and multi-label classification tasks. The multi-class classification is based on Hungarian and English texts. I have an ongoing project, where I created an LSTM and this LSTM is able to classify the text for cryptocurrencies. 

Pre-trained models are very important in terms of flow. It's a great opportunity to create very fast, new, deep learning models. 

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. 

I don't have too much experience with the dashboards in the solution, however, it's possible they could be improved.

I need to have more experience in the security aspect of the solution. It could, however, always develop this area more.

It would be nice if the solution was in Hungarian. I would like more Hungarian NLP models. 

I've used the solution in the past 12 months. 

I haven't had any issues with stability so far. It's really reliable. There aren't issues with bugs or glitches or crashing.

The solution is absolutely scalable. My understanding of scalability is that when it comes to the solution, the learning task should run on the selected CPU. If I know how it should be and how it should be run, it's very easy as TensorFlow can also run on one CPU core or even on a GPU and so on.

Colab is great when I would like to learn something. You end up using Colab a lot. I like Jupyter Notebook and use it to create TensorFlow models.

There's also a lot of good documentation you can use to reference things and learn about the solution.

I have a bit of knowledge of PyTorch. I haven't used it much, however. I'm learning a bit about it now. While I don't have practical experience, I am taking a Coursera course that uses it.

In university, maybe ten years ago, I might have used something in MATLAB. I didn't have too much experience or too much knowledge about deep learning at that time, so I cannot say if it's hard or easy to do tasks in MATLAB or to compare the two. 

The initial setup isn't too complex. It's pretty straightforward. There is a lot of good documentation. There are many good courses. There are many good books from professors - from Ph.D.'s to data scientists. It's very, very easy. It's not so complex.

The deployment didn't take a very long time. In my experience, it only really took a few days. That is if a baseline model is enough for the client. Of course, if the requirement is an optimized model, it can be weeks or even a month.

The data processing, hyperparameter tuning, CPUs, and GPUs are all very, very important. If I have a very, very strong machine, I can do everything very fast, and it's a huge help for me.

I don't pay for the solution.

It's my understanding that if you want technical support you need to pay.

I'm just a user. I'm not a reseller or consultant.

I'm learning TensorFlow so I would like to do a TensorFlow certificate from Google in January or February. I'm learning how to deploy Poker with TensorFlow. It's new territory for me, however, it is very important.

I'm not sure which version of the solution I'm using. I have more developed servers and I'm using different versions.

I can recommend TensorFlow to anybody that wants to create deep learning models.

I'd rate the solution ten out of ten. I've been quite happy with it so far. 

I use this solution out of personal interest — for AI.

Our company doesn't use it too much. We tend to use OpenML, which is included in the OpenCV package, OpenML. 

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

I tend to find it to be a bit too much orientated to AI itself for other use cases, which is fine — that's what it's designed for. Personally, I find it to be a bit too much AI-oriented.

We have been using TensorFlow for roughly five years.

We haven't had any issues stability-wise or scalability-wise. I tend to use it with Python. It seems okay. It works fine. 

The initial setup was straightforward.

I would happily recommend TensorFlow to people who are looking to use it for AI. Overall, on a scale from one to ten, I would give this solution a rating of eight.