What is TensorFlow?
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 used across many other scientific domains.
TensorFlow Buyer's Guide
Download the TensorFlow Buyer's Guide including reviews and more. Updated: May 2021
TensorFlow CustomersAirbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
What users are saying about TensorFlow pricing:
- "I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."
- "TensorFlow is free."
- "It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."
- "We are using the free version."
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The generator saves us a lot of time and memory in terms of development and the learning process of models
What is our primary use case?The main purpose of TensorFlow is to develop neural networks for data science projects. For example, I had a project about a super-resolution GAN, which is a model that you give a low-resolution image, and it will complete the details for you. I used Keras and TensorFlow for this model and it was really easy to use. The time to implement was simply minimal in comparison to the time for testing, logic, and high-level implementation. That was the highlight of my academic project. For a client, I used TensorFlow and Keras to develop a predictive heat map for orders. He wanted to build a… more »
Pros and Cons
- "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, it improved the lives of many people around the globe."
- "However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
What other advice do I have?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…
Great for deep learning, accelerates Training/Inference, and is quite stable
What is our primary use case?In one of my latest projects, I used convolutional neural networks along with several other models for Finance; The objective was to predict future Close Price of S&P500 index. And in the end, I discovered that an ensemble model of convolutional neural networks works the best; I got a very low error and pretty good accuracy. That was my most recent project. Another project that I used the solution for was using convolutional neural networks was in visual recognition in which the goal was to take a picture of somethin. Then the model would recognize what the images are. That's the pretty… more »
Pros and Cons
- "TensorFlow is a framework that makes it really easy to use for deep learning."
What other advice do I have?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…
Learn what your peers think about TensorFlow. Get advice and tips from experienced pros sharing their opinions. Updated: May 2021.
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BEKKOUCHE Imad Eddine Ibrahim
Computer Vision Engineer at Innopolis University
Enables us to accomplish faster training and deployment
What is our primary use case?I worked for a French company. They used TensorFlow for image classification. after that, I started working with a Russian-American Company who used TensorFlow mainly for object detection. TensorFlow is very good at object detection. We also used it once for natural language processing and audio processing, but I was not directly involved in that project. I was just assisting with deployment issues. We have some clients which wanted us to deploy on the cloud. Alternatively, some clients are releasing Tenserflow on some new edge devices, as an alternative to deploying on the cloud. It is going… more »
Pros and Cons
- "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."
- "It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
What other advice do I have?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…
Owner, AI Entrepreneur, Consultant, Team Leader, Machine Learning & NLP Engineer at intelligentbusiness.hu
Great feature sets, works well with Docker and offers good documentation
What is our primary use case?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 text. I have an ongoing project, where I created an LSTM and this LSTM is able to classify the text for cryptocurrencies.
Pros and Cons
- "Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
- "It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
What other advice do I have?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.
Easy to set up with great documentation and good stability
What is our primary use case?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… more »
Pros and Cons
- "Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
- "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."
What other advice do I have?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…
Managing Director at Geeky Bee AI
Real UserTop 5
Nov 30, 2020
Deep learning library that provides a set of functions like feature mapping and feature extraction
What is our primary use case?We have a project that a Canada-based client is expecting us to develop. If there is a hardware product, it's a mirror LCD device, that is installed in your home and when you start doing an exercise, our AI algorithm will detect what kind of exercise, whether you're doing pushups, jump, etc. We also detect what kind of hardware equipment is being used. We also use TensorFlow to count.
Pros and Cons
- "TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
- "In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
What other advice do I have?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.
Data Scientist at a university with 5,001-10,000 employees
Real UserTop 10
Mar 31, 2021
Super scalable, awesome stability, open-source, and cost-effective
What is our primary use case?With TensorFlow, it is all just personal research that I've done. I'm hoping to bring it to work. TensorFlow is one of the most commonly used platforms for machine learning and deep learning. I specialize in natural language processing and computer vision. Right now, a lot of the clientele work that I have is basic data science of just cleaning and managing data and getting it to fit. I am planning to give a nice example of what we could do by building models that actually predict things that they're looking to do. The models that they have right now are literally just basic, statistical, and… more »
Pros and Cons
- "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."
- "It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
What other advice do I have?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…
Project Manager at INFOCOM Ltd
Real UserTop 10
Nov 30, 2020
Open-source, good documentation, easy to set up, and it's reliable
What is our primary use case?I use this solution to create Neural Networks, which are computer algorithms for the recognition of objects. This is done based on the SL object that predefines it. Most of our experience is computer related, but in most cases, we work with images.
Pros and Cons
- "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."
- "There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
What other advice do I have?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.
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