The platform integrates well with other tools, especially Python, which we use to create models. These models can be deployed on mobile devices, which perfectly suits our requirements. It supports our AI-driven initiatives very well by producing AI models, which is its primary function. I recommend it for those seeking specialized scripting. However, it's important to consider other options as well. It is better suited for specialists in the field and is less user-friendly than general tools like Excel. I rate it overall at six out of ten. While it is a powerful tool, other software options are slightly simpler for training models.
It stands out because of its multi-language support, a feature that I find particularly valuable. If you're considering Level Zero Group services or a similar integration, TensorFlow offers seamless integration, making it a valuable asset for projects involving built-out infrastructure, especially when compared to alternatives lacking this integration. It provides a robust set of tools, including a visualization tool for generating and debugging models. This tool offers deep insights into the models, contributing to a streamlined recommendation process. Speaking of scalability and stability, it outperforms its counterparts, making it a preferred choice. Its user-friendly interface and ease of learning contribute to its overall appeal. I would rate it nine out of ten.
I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist. In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation. Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant. I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.
If, like us, you're interested in one AI framework that runs both on edge and central computing, then definitely look into TensorFlow. In other words, if you need AI to extend right into the edge devices, TensorFlow is super. I rate TensorFlow an overall nine out of ten.
Sales Account Manager Southern Europe, MEA and Turkey at a computer software company with 51-200 employees
Real User
Top 10
2023-02-21T16:32:00Z
Feb 21, 2023
I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that. My advice to anyone willing to try the solution is that you need to do a lot of research on GitHub, AI websites, or other websites where people exchange their knowledge.
Data Science Lead at a mining and metals company with 10,001+ employees
Real User
2022-08-04T20:54:14Z
Aug 4, 2022
I would recommend this solution to others. There are a lot of companies, that create new packages around TensorFlow, they make it easier to use, but whenever you make it easier to use, you create some limitations. I rate TensorFlow a nine out of ten.
Data Scientist at a university with 5,001-10,000 employees
Real User
2021-03-29T23:53:31Z
Mar 29, 2021
I would definitely advise understanding your data and what you're doing because it may not be worth the time if you're going to dive deep into Deep Neural Networks or even just basic Convolutional Neural Networks when you don't really need to. What's the point of building a regressor that is going to be scalable with TensorFlow if all you're trying to do is basic statistics? It depends on the size of the data science work that you're doing. You can just use your local machine with the open-source TensorFlow and create pretty good models. Getting it into production depends on the security of the system. I don't know what the data engineers are going to have to do to close the pipelines. I would rate TensorFlow a ten out of ten any day.
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.
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.
Machine Learning Engineer, AI Consultant at intelligentbusiness.hu
Real User
Top 20
2020-12-07T16:25:36Z
Dec 7, 2020
I'm just a user. I'm not a reseller or consultant. I'm learning on TensorFlow so I would like to do a TensorFlow certificate by Google in January or February. I'm learning now to deploy with 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.
There are always new versions coming out and some versions have issues while some versions don't. When you deploy with the latest version, just make sure that all the systems work as expected when you're deploying. I would rate TensorFlow an eight out of ten.
I would recommend TensorFlow for techniques that need to develop Neural Networks. I would also recommend PyTorch. I would rate this solution a nine out of ten.
I primarily work on Google Colab in which everything is installed. The most recent versions of TensorFlow are already installed on the Colab. I have written a deep learning library, which is like very much TensorFlow and PyTorch. It's like my own miniature version of TensorFlow, which I have written as it was an academic project. TensorFlow hides all the details of like nitty-gritty details like how is it working, how the matrices are being multiplied, how is it being handled on GPUs? All these details have been abstracted. If you're writing a model in TensorFlow, you will write just five lines of code (in Sqequential API), in these five lines everything is happening. When I was developing that library, I learned that those five lines of code acutally map to, say, a 1000 lines of code underneaththe hood. I actually wrote all that in order to learn how exactly it works. I just learned what those a thousand lines are. TensorFlow is just a tool for deep learning. You can use a complete model, which will recognize images in five lines of code. However, to really do deep learning you have to go underneath the hood and understand how exactly things are working. If you are coming from a different background and you write five lines and you can do a model, that's great, however, for example, if a debugging problem comes, you will be in much better shape if you have learned what's underneath. You will be much better shaped to debug your code. If you better understand your code you can better optimize your code. TensorFlow provides you a layer of abstraction, however, that layer of abstraction is bad in some ways. Primarily, I'm a machine learning engineer. Most of my projects are on using TensorFlow, from my perspective I use TensorFlow a lot and PyTorch occasionally. I also am a full stack developer, I develop apps using React, Django, and D3 yet I don't work a lot in that area. Primarily I work from TensorFlow. From my perspective, they are widely used. Overall, I would rate the solution ten out of ten. It's very good.
I had a problem with it during one implementation. I assumed that the data would be small. I think before implementing your TensorFlow model, it's crucial to note what is the size of your data and will it increase in the future? Usually, a developer wants to develop the model as easily as can it be. So they just tend to load all the data in memory and then run it into a flow model. So that is really problematic if your data is huge. That's why it's best for the developer before they write any line of code to check the data. If it doesn't fit in memory, they can use the TensorFlow functionality of load from a generator and this way they can actually have just one image in the memory at a time per thread. So it's really amazing. So I think that that is the tweak that I would advise developers to have, before developing their model. I would rate TensorFlow 9 out of 10.
Have a look at TensorFlow extended. It's very useful. Especially if you know how to use the old system. It will speed up the process of deploying your model. Don't reinvent the wheel. There's always going to be a good GitHub repo out there which kind of answers your solution. You shouldn't really spend a lot of time trying to build the new models where there is some other open source project that actually did a good job of the modelling part. You definitely need to have your own pipelines for this process. Try to build the pipelines that automate most of the tasks for you. Then all you need to concern yourself with is just the architecture. Obtain a pipeline template from GitHub of what you are trying to achieve, amend it for your needs and then you are ready to go. Your model is training already. I would rate TensorFlow 8 out of 10.
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is...
The platform integrates well with other tools, especially Python, which we use to create models. These models can be deployed on mobile devices, which perfectly suits our requirements. It supports our AI-driven initiatives very well by producing AI models, which is its primary function. I recommend it for those seeking specialized scripting. However, it's important to consider other options as well. It is better suited for specialists in the field and is less user-friendly than general tools like Excel. I rate it overall at six out of ten. While it is a powerful tool, other software options are slightly simpler for training models.
I recommend using TensorFlow. Its libraries make Python programming smoother and reduce the workload. It is easy to use and learn. I rate it an eight.
It stands out because of its multi-language support, a feature that I find particularly valuable. If you're considering Level Zero Group services or a similar integration, TensorFlow offers seamless integration, making it a valuable asset for projects involving built-out infrastructure, especially when compared to alternatives lacking this integration. It provides a robust set of tools, including a visualization tool for generating and debugging models. This tool offers deep insights into the models, contributing to a streamlined recommendation process. Speaking of scalability and stability, it outperforms its counterparts, making it a preferred choice. Its user-friendly interface and ease of learning contribute to its overall appeal. I would rate it nine out of ten.
It is a recommended solution but at the beginner level, they should have some assistance. I rate the overall solution a ten out of ten.
I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist. In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation. Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant. I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.
If, like us, you're interested in one AI framework that runs both on edge and central computing, then definitely look into TensorFlow. In other words, if you need AI to extend right into the edge devices, TensorFlow is super. I rate TensorFlow an overall nine out of ten.
I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that. My advice to anyone willing to try the solution is that you need to do a lot of research on GitHub, AI websites, or other websites where people exchange their knowledge.
I would recommend this solution to others. There are a lot of companies, that create new packages around TensorFlow, they make it easier to use, but whenever you make it easier to use, you create some limitations. I rate TensorFlow a nine out of ten.
I rate TensorFlow a ten out of ten.
I would definitely advise understanding your data and what you're doing because it may not be worth the time if you're going to dive deep into Deep Neural Networks or even just basic Convolutional Neural Networks when you don't really need to. What's the point of building a regressor that is going to be scalable with TensorFlow if all you're trying to do is basic statistics? It depends on the size of the data science work that you're doing. You can just use your local machine with the open-source TensorFlow and create pretty good models. Getting it into production depends on the security of the system. I don't know what the data engineers are going to have to do to close the pipelines. I would rate TensorFlow a ten out of ten any day.
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.
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'm just a user. I'm not a reseller or consultant. I'm learning on TensorFlow so I would like to do a TensorFlow certificate by Google in January or February. I'm learning now to deploy with 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.
There are always new versions coming out and some versions have issues while some versions don't. When you deploy with the latest version, just make sure that all the systems work as expected when you're deploying. I would rate TensorFlow an eight out of ten.
I would recommend TensorFlow for techniques that need to develop Neural Networks. I would also recommend PyTorch. I would rate this solution a nine out of ten.
I primarily work on Google Colab in which everything is installed. The most recent versions of TensorFlow are already installed on the Colab. I have written a deep learning library, which is like very much TensorFlow and PyTorch. It's like my own miniature version of TensorFlow, which I have written as it was an academic project. TensorFlow hides all the details of like nitty-gritty details like how is it working, how the matrices are being multiplied, how is it being handled on GPUs? All these details have been abstracted. If you're writing a model in TensorFlow, you will write just five lines of code (in Sqequential API), in these five lines everything is happening. When I was developing that library, I learned that those five lines of code acutally map to, say, a 1000 lines of code underneaththe hood. I actually wrote all that in order to learn how exactly it works. I just learned what those a thousand lines are. TensorFlow is just a tool for deep learning. You can use a complete model, which will recognize images in five lines of code. However, to really do deep learning you have to go underneath the hood and understand how exactly things are working. If you are coming from a different background and you write five lines and you can do a model, that's great, however, for example, if a debugging problem comes, you will be in much better shape if you have learned what's underneath. You will be much better shaped to debug your code. If you better understand your code you can better optimize your code. TensorFlow provides you a layer of abstraction, however, that layer of abstraction is bad in some ways. Primarily, I'm a machine learning engineer. Most of my projects are on using TensorFlow, from my perspective I use TensorFlow a lot and PyTorch occasionally. I also am a full stack developer, I develop apps using React, Django, and D3 yet I don't work a lot in that area. Primarily I work from TensorFlow. From my perspective, they are widely used. Overall, I would rate the solution ten out of ten. It's very good.
I had a problem with it during one implementation. I assumed that the data would be small. I think before implementing your TensorFlow model, it's crucial to note what is the size of your data and will it increase in the future? Usually, a developer wants to develop the model as easily as can it be. So they just tend to load all the data in memory and then run it into a flow model. So that is really problematic if your data is huge. That's why it's best for the developer before they write any line of code to check the data. If it doesn't fit in memory, they can use the TensorFlow functionality of load from a generator and this way they can actually have just one image in the memory at a time per thread. So it's really amazing. So I think that that is the tweak that I would advise developers to have, before developing their model. I would rate TensorFlow 9 out of 10.
Have a look at TensorFlow extended. It's very useful. Especially if you know how to use the old system. It will speed up the process of deploying your model. Don't reinvent the wheel. There's always going to be a good GitHub repo out there which kind of answers your solution. You shouldn't really spend a lot of time trying to build the new models where there is some other open source project that actually did a good job of the modelling part. You definitely need to have your own pipelines for this process. Try to build the pipelines that automate most of the tasks for you. Then all you need to concern yourself with is just the architecture. Obtain a pipeline template from GitHub of what you are trying to achieve, amend it for your needs and then you are ready to go. Your model is training already. I would rate TensorFlow 8 out of 10.