80 reviews

TensorFlow

Open-source platform for creating ML-enabled applications

4,7 /5 (80 reviews) Write a Review!
Overall rating
4,7
/
5
Value for Money
4,7
Features
4,6
Ease of Use
3,9
Customer Support
4,1
99% recommended this app
80 reviews
Scott w. D.
Industry: Management Consulting
Company size: Self Employed

A Machine and Deep Learner must have Library

Used Daily for 2+ years
Reviewed on 2018/09/05
Review Source: Capterra

Pros

This Library is very flexible for doing Matrices and Tensor So building very deep high level but quick and scalable ready to use neural networks is at your finger tips.

The added other Anaconda Library and Keras compatibility

Cons

Depreciation of the code is frustrating. To use one form just to throw a Error message.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 10.0/10

Ben W.
Industry: Computer Software
Company size: 11-50 Employees

Relatively Straightforward Deep Learning Framework

Used Weekly for 1-5 months
Reviewed on 2019/09/27
Review Source: Capterra

Human pattern recognization, image recognization. Habits and trends.

Pros

The 2.0 version is easy to set up and there are a lot of APIs that are integrated for using various programming languages to do the same thing. I personally have been using python with this application and have had very little problems getting going. There are a lot of tutorials on getting started, some good data available for free to assist with the learning process. Everything can be run locally which makes it easy to expand on-site. Cloud options are also affordable.

Cons

The learning curve is a bit steep. This isn't specifically an issue because of TensorFlow itself, the idea of neural networks are not simple. TensorFlow has made improvements on 2.0, that make it easier to use compared to previous versions.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Verified Reviewer
Industry: Information Technology & Services
Company size: 501-1 000 Employees

One of the best Deep learning frameworkes

Used Weekly for 2+ years
Reviewed on 2020/09/24
Review Source: Capterra

There are many implemented models in TF and the community is big, so in general when you have some problems it's very easy to find the solutions.

Pros

There are many different options in TF to build a neural network, different layers are implemented, different loss functions. With the TF graph it's easy te debug further errors. TF is also very helpful when deploying a models.

Cons

It's not the easiest way to learn deep learning. I didn't use the v2 of Tensorflow, but the previous one has some difficult structure, if you are new in the industry.

Rating breakdown

Ease of Use

Likelihood to recommend: 7.0/10

Verified Reviewer
Industry: Financial Services
Company size: 11-50 Employees

Overhyped application

Used Daily for 6-12 months
Reviewed on 2018/12/03
Review Source: Capterra

I use tensorflow for machine learning apps to find correlations in the market, but the app has let me down and I have since moved on to other libraries, as tensorFlow was simply to difficult to use.

Pros

Tensorflow is a good library for machine learning, but only for more experienced developpers.

Cons

It is very hyped by the community, but has a teap learning curve and is hard to learn. So the app is not beginner friendly, but also is't the best library for high level machine learning.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 2.0/10

Silviu O.
Industry: Computer Software
Company size: 2-10 Employees

A great foundation for Machine Learning

Used Other for 6-12 months
Reviewed on 2020/01/06
Review Source: Capterra

TensorFlow is a great initiative and a great product. It can be intimidating at first, but once mastered it can offer a great advantage. The best part is that it covers a great range of machine learning use cases from supervised to unsupervised learning and great support for lots of languages and integration. Great community support and a great vision ahead.

Pros

First of all, it's free. Secondly, being developed by Google it integrates easily with Google ML the other products. At the time of its release, it has come with great enthusiasm thus it has a great community build around it.

Cons

It is fairly difficult at first, as it brings the whole complexity of working with machine learning. It is very resource-driven and thus the only viable option is using it in the cloud.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 10.0/10

Esra K.

Very helpful in the new world of machine learning.

Used Daily for 6-12 months
Reviewed on 2018/05/11
Review Source: Capterra

You will learn a lot from TensorFlow. It is a good way of entering the machine learning world.

Pros

I used TensorFlow on AWS which was easier with all the infrastructure AWS built. It was a good start to machine learning with all the AI and neural network popularity going on these days.

It was challenging and exciting to prepare datasets, train them and see the satisfactory results in dashboard.

It is also open source and this gives an advantage to TensorFlow.

Cons

There is a long and challenging learning period. Documentation is rich but it would be so much better to learn and use it with some visual aids.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Volodin A.
Industry: Computer Software
Company size: 201-500 Employees

Best performance for ML tasks

Used Weekly for 2+ years
Reviewed on 2018/12/12
Review Source: Capterra

I often work with ML engine, and it appears very complex to me. Because of that I suggest Newbies to start with AutoML first.

Pros

ML and AutoML by google dramatically simplify work of Machine Learning developers, in my opinion. Google provides a complete infrastructure that can import export, train and deploy model within the ML environment. On the other hand AutoML provides even more simplicity with operations.

Cons

It is often difficult to implement ML solution and require time and efforts that are not always available due to certain constraints.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 10.0/10

Verified Reviewer
Industry: Computer Software
Company size: Self Employed

TensorFlow is a efficient tool for Machine learning task

Used Other for 1+ year
Reviewed on 2020/02/21
Review Source: Capterra

I built my machine learning project using TensorFlow. Initially, I faced some installation issues in windows. Then I installed and used it in Linux. It was compatible with Linux and easy to learn.

Pros

It has a rich visualization facility and frequent updates to add new additional features. Tensorflow is simpler than other libraries like Torch and Theano.

Cons

It’s not speeder than other libraries and it makes problems in the windows operating system. It also doesn’t collaborate with other frameworks like OpenCL.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 8.0/10

M. serhat D.
Industry: E-Learning
Company size: 1 001-5 000 Employees

Era of machine learning

Used Monthly for 2+ years
Reviewed on 2020/04/02
Review Source: Capterra

If you are planning to kick-start your product idea including machine-learning functionality, then Tensorflow is the first product you should take a look at.

Pros

Huge community, a rich documentation, easy to integrate with other software, free and open-source, powerful features, constantly getting better with community contributions.

Cons

The learning curve is a little bit steep, however, there are many free resources on the Internet. The product is getting lots of changes and contributions from the community, so it's hard to follow what is going on with the product all the time.

Rating breakdown

Ease of Use

Likelihood to recommend: 9.0/10

Vibhor A.
Industry: Research
Company size: 5 001-10 000 Employees

Great for scaling your Machine Learning needs

Used Weekly for 6-12 months
Reviewed on 2020/02/25
Review Source: Capterra

Great for anyone starting to use ML as a analytical tool. It provides nee=cessary training for you to move forward

Pros

Ease of use, adaptability, and speed associated with the cloud platform is amazing. It can help solve any research problems

Cons

It uses standard template whcih might be difficult to customize in special needs scenario. Some odf the functionality is locked out limiting usage

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 8.0/10

Verified Reviewer
Industry: Computer Networking
Company size: 10 000+ Employees

Google Cloud ML Engine review

Used Daily for 1+ year
Reviewed on 2019/11/14
Review Source: Capterra

In constructing ML project at first, it is run by the local hardware platform Tensorflow GPU version, so that at the time of training can speed up a lot, but because of the high cost of GPU, when a project order of magnitude increases, the training time of exponential growth, if want to reduce the time, only through optimization algorithm or hardware.After that, I moved the whole project to the cloud platform for operation. Of course, there was also a problem. The resources of Aliyun were all based on fixed configuration to determine different prices.Finally, I migrated to Google Cloud ML Engine, which was cheap and perfectly compatible with other Google products, such as Cloud Storage, Cloud Dataflow, and Cloud Datalab.For the extension of the project and late derivative, provides a great convenience.

Pros

It doesn't take up any of my local computer resources, just throw a command and let the Google cloud run when I need to run, and it doesn't block any of my other work.
The software provides mainstream training model, prediction model, mainstream ML framework to accelerate the efficiency of our project development.
Low price, suitable for early learning and research.

Cons

The threshold of software use is relatively high, and the background of Python or Tensorflow is required, so it is difficult to get started.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Luigi V.
Industry: Defense & Space
Company size: 51-200 Employees

ML must have

Used Daily for 2+ years
Reviewed on 2019/09/27
Review Source: Capterra

Definitely my first option when neural networks are involved in my personal and professional research. Furthermore it also has High-Level API (Keras) which make everything easier.

Pros

Mastering tenworflow unlocks in you all the possibilities you can have with the current Machine Learning techniques. Data Science, Computer Vision, Machine Learning, NLP...name any area of research of AI, tensorflow can easily handle it.

Cons

It might be a little too complicated at first, especially if you are a beginner of Neural Networks. But sticking with it will give you the ability to interact with it.

Rating breakdown

Ease of Use

Likelihood to recommend: 8.0/10

Verified Reviewer
Industry: Biotechnology
Company size: 10 000+ Employees

simplicity while being resourceful

Used Daily for 1+ year
Reviewed on 2019/12/12
Review Source: Capterra

ability to do machine learning in the cloud with the ability to monitor data quality and also transform data along the way to serve optimal results in ML models.

Pros

Simplicity, speed and very low latency of performance are the best parts of google cloud ML. It also has the ability to manage the end to end process in machine learning while also giving the ability to store data and importantly tools to monitor data quality along the ML journey.

Cons

This is a fully cloud based solution and hence for most optimal performance the data also needs to be in google cloud - I wish there was an on prem version of this product since we are hybrid and have data both on prem and in the cloud.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Verified Reviewer
Industry: Computer Software
Company size: 51-200 Employees

Tensorflow is the future of our business, and likely the future of machine learning modeling.

Used Daily for 1+ year
Reviewed on 2018/03/07
Review Source: Capterra

Tensorflow is the future of machine learning modeling. There is no way around that and we as a company are fortunate to bring this technology to the forefront.

Pros

Tensorflow is the easiest way to implement machine learning software into your product/business. The repository is colossal and there is an abundance of support within the community alone. Tensorflow is updating regularly and will continue to grow in the years to come.

Cons

Hardware is a common bottleneck in machine learning software. We have built out dedicated computing space just for our tensorflow models and will have to continue to upgrade and expand that space. It's just the nature of the business.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 10.0/10

Verified Reviewer
Industry: Retail

Gold standard for ML libraries

Used Daily for 1+ year
Reviewed on 2018/05/02
Review Source: Capterra

Tensorflow is a one stop shop for most machine learning applications. The ease of use isn't really there but once you learn the processes required, everything falls into place. We use it to train various machine learning models and couldn't be happier.

Pros

This is the industry leading machine learning library. It's essential for any deep learning models you're looking to implement. The repository is large and very thorough. We've trained many datasets on various models using tensorflow and couldn't be happier.

Cons

It's not easy to use. This is the case for most emerging technologies though, the learning curve is dramatic but such is the cost of new tech.

Rating breakdown

Ease of Use

Likelihood to recommend: 10.0/10

Thomas Y.

TensorFlow is useful, although it requires a healthy time commitment to produce accurate models

Used Weekly for 2+ years
Reviewed on 2018/04/26
Review Source: Capterra

The benefits I received from this software is more accurate modeling and an interesting insight into what makes one software better than another. TensorFlow did for me what it says it does - produce high quality models, such as neural networks, with a lot of human capital input.

Pros

TensorFlow is fascinating in seeing how it produces results in a reasonable time frame. It is completely flexible compared to its costly competitors. The software connects well with various data sources and in setting up scripts to run automatically.

Cons

TensorFlow takes a lot of time to become an expert in what it is doing. The programming time-commitment might not be worth it unless you plan on customizing your modeling to work with other software.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 7.0/10

Deepak kumar S.
Industry: Information Technology & Services
Company size: 201-500 Employees

TensorFlow is must use thing for deep learning

Used Other for 1+ year
Reviewed on 2019/01/01
Review Source: Capterra

A must use library to develop new algorithm of deep learning as it gives you power to customise everything.

Pros

I think if you are developing something that includes deep learning and some other machine learning technique as well, then you should use TensorFlow. It comes with lot of inbuilt functionality which makes thing easy for deep learning algorithm. It comes with inbuilt data handling/processing techniques which is very useful when developing new algorithm and implementing them.

Cons

Ti has steep learning curve. Understanding it's concept of tensor is not easy to understand. It takes time to learn it completely. And even after learning it, it takes time to develop or write the implementation of deep learning algorithm as you have to write everything by your own.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Sean B.
Industry: Computer Software
Company size: Self Employed

Incredibly powerful

Used Weekly for 1+ year
Reviewed on 2019/12/05
Review Source: Capterra

The framework has been amazing for me both for getting into machine learning and for developing more advanced projects.

Pros

The software is not the easiest to grasp but there are myriad amounts of documentation and examples online which can help with most situations. The Github repo is also well maintained with references to any bugs and problems that one may encounter

Cons

Debugging is incredibly difficult with version 1 of the framework (this is meant to be addressed in version 2) and can take a long time to get a handle of the particular concepts. The complete library is exhaustive but to the point of abstracting certain concepts too much.

Rating breakdown

Value for Money
Ease of Use

Likelihood to recommend: 9.0/10

Verified Reviewer
Industry: Computer Software
Company size: 2-10 Employees

Tensorflow review

Used Daily for 1+ year
Reviewed on 2019/05/17
Review Source: Capterra

Built an image classifier

Pros

It's fast, can be deployed on different platforms and has multiple functionalities, it's mostly used for research works

Cons

It's not too easy to learn, project done using tensorflow are not always production ready

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 7.0/10

Verified Reviewer
Industry: Higher Education

A powerful high-level machine learning library!

Used Weekly for 6-12 months
Reviewed on 2018/04/19
Review Source: Capterra

Pros

Tensorflow is a high-level machine learning library. I can use it to design neural network structures without writing C++ or CUDA18 code in order to get high efficiency. It supports automatically calculating derivative. Tensorflow is implemented with C++ and it uses C++ to simplify online deployment. In addition to C++ interface, it also provides us with Python, Java and Go interfaces.

Cons

Although Python is very powerful and easy to use, using Python with TensorFlow will still cause some efficiency problems. For example, every mini-batch needs to be fed from Python to the network. During this process, when the data size of mini-batch is small or calculation time of is short, it will cause long latency.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Shriya B.
Industry: Information Technology & Services
Company size: 201-500 Employees

Most advance machine learning library

Used Other for 1+ year
Reviewed on 2018/09/03
Review Source: Capterra

Building machine learning model from scratch and want full power of customisation then choose this tool.

Pros

I think it is the most advance library for machine learning specially for deep learning. It very easy to write neural network in this library. It comes with lot of inbuilt function to process data. Also, it has lots of prebuilt function which ease the implementation of neural network.

Cons

There is no bad thing about this but initially it takes lot of time to understand it as it works on tensors instead of simple vector or array object. But once you learn this, it will be easy to write code.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Shalinee S.
Company size: 201-500 Employees

Made the deep learning kids work

Used Other for 2+ years
Reviewed on 2018/07/11
Review Source: Capterra

Best library for deep learning

Pros

This library is the best for deep learning. Designing neural network with this library is very easy. Also, it compute things very fast. It has made the visualization very easy. It has lots of built in features like conv2d network, lstm etc.

Cons

It's the best thing to do stuff in deep learning but it require a long learning curve. But once you know how it works then it made your job very easy.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Bhargavi C.
Industry: Computer Software
Company size: 10 000+ Employees

Rapid prototyping of neural network models which is a great learning resource for students

Used Daily for 6-12 months
Reviewed on 2018/03/29
Review Source: Capterra

Rich community support and learning resources.

Pros

Has rich resources to support and help in learning the nuances of Neural Networks and Deep Learning and helps in rapid initial prototyping

Cons

Has a steep initial learning curve and is not high level programming system like Pytorch. Would require more effort in defining the the different modules of the project.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10

Verified Reviewer
Industry: Automotive
Company size: 2-10 Employees

I adore this

Used Daily for 1+ year
Reviewed on 2018/09/26
Review Source: Capterra

Pros

Great way to have all in one place- cakendar,docs,calculations. It makes my work do much easier and convenient.

Cons

It has all I need in one place,so no flaws

Rating breakdown

Ease of Use

Likelihood to recommend: 8.0/10

Verified Reviewer
Industry: Computer Software
Company size: Self Employed

Review about Tensorflow

Used Daily for 6-12 months
Reviewed on 2019/09/16
Review Source: Capterra

I have used it to my image processing and machine learning tasks

Pros

It has grate graph visualizations facility and multiple language support than other libraries. And also faster than Torch and Theano backends.

Cons

The development team frequently release versions. This rapid changes make difficulties to follow the code.

Rating breakdown

Value for Money
Ease of Use
Customer Support

Likelihood to recommend: 9.0/10