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External reviews

41 reviews
from G2

External reviews are not included in the AWS star rating for the product.


    Information Technology and Services

TensorFlow with Keras over Spark is a great solution

  • March 20, 2018
  • Review provided by G2

What do you like best?
Easy of creating models and parameterizing them
What do you dislike?
Need experienced programmers to set it up and train the models
What problems are you solving with the product? What benefits have you realized?
Predictive Analytics
Resource optimization
Recommendations to others considering the product:
Include Keras to further automate


    Naveen K.

Simple, Fast and easy

  • February 02, 2018
  • Review provided by G2

What do you like best?
Efficiency and the ease of deployment during projects
What do you dislike?
Boot up time and the sometimes the clumsiness
What problems are you solving with the product? What benefits have you realized?
Data Management


    Krishnan V.

Really good for AI/ML based use cases

  • February 02, 2018
  • Review provided by G2

What do you like best?
Helps setting up the neural network well - strongly recommend it

We have been using internally and evaluating how to use it.
What do you dislike?
Don't really dislike anything in it - maybe more flexibility
What problems are you solving with the product? What benefits have you realized?
- Better time to market
- Easier to deploy solutions in the market
- Develop newer capabilities
Recommendations to others considering the product:
Decide what are the use cases before taking up the platform. Fairly intuitive and easy to use


    Information Technology and Services

Simple way to build complex models

  • February 02, 2018
  • Review provided by G2

What do you like best?
I like how easy tensor flow makes it to build a simple neural network. You can have a model up and running in minutes, but tensorflow still provides advanced users with the ability to customize models a lot.
What do you dislike?
I wish that the documentation for tensorflow was more detailed. Sometimes I have a hard time finding answers to some questions that I have about tensorflow.
What problems are you solving with the product? What benefits have you realized?
I am trying to make more accurate predictive models in the credit industry. Tensorflow has helped to improve the accuracy of existing models built using other software by a few percent.


    Hospital & Health Care

Best deep learning software

  • February 01, 2018
  • Review provided by G2

What do you like best?
Good documentation, large support community
What do you dislike?
learning curve with backgrounds like Java, DotNet and Javascript etc
What problems are you solving with the product? What benefits have you realized?
Deep learning models to predict certain outcomes
Recommendations to others considering the product:
Ability to build models and train them easily with Tensorflow. Best tool for data scientists


    Computer Software

The machine learning swiss army knife

  • January 30, 2018
  • Review provided by G2

What do you like best?
The ability to easily write code that scales up to multiple CPUs/GPUs. Since version 1.0, there are higher level modules that provide Keras-like functionalities (e.g. layers, metrics). Moreover, it is definitely production ready. The best thing, though, is the Tensorboard tool that can be used to debug the implemented algorithms.
What do you dislike?
Setting up GPU support on Windows can be tricky, depending on how convoluted the environment is. While Tensorboard is a great tool, sometimes its pages get "stuck" and force to reload the tensorboard server again. The learning curve might be steeper than Keras, which hides most of the complexity away.
What problems are you solving with the product? What benefits have you realized?
TensorFlow it's a nice, low/mid level API to build production-level software that deals with machine learning. It's stable, so building products at scale for production use is not a problem. This is being used as the back-end to train voice recognition models.
Recommendations to others considering the product:
I'd strongly suggest using Keras for prototyping before jumping straight into TensorFlow. Getting started with keras is much simpler and paves the way to implementing the final product with TensorFlow.


    Medical Devices

Best deep learning open source library out there

  • January 25, 2018
  • Review provided by G2

What do you like best?
TensorFlow uses python which is conenient.Its numerical compability with NumPy is great. We can store the models and reuse them is very convenient
What do you dislike?
The learning curve is a bit steep.The tutorials on the website can be more explicitly explained.
What problems are you solving with the product? What benefits have you realized?
I use tensor flow to solve complex classification problems especially delaying with Images,as Image processing and machine/deep learning go hand in hand for classification problems
Recommendations to others considering the product:
Get yore basics of python,numpy and ML strong before using TensorFlow


    Education Management

Amazing

  • October 24, 2017
  • Review provided by G2

What do you like best?
The easy environment to adapt.
Working online community is awesome...
Thank you guys
What do you dislike?
Some dependencies are that easy to work with,
RNNs are still a bit lacking, compared to Theano.
What problems are you solving with the product? What benefits have you realized?
designing a hardware to help the blind peoples recognize there loved ones
Recommendations to others considering the product:
Theano


    Raul G.

Great tool, worth the steep learning curve

  • October 20, 2017
  • Review verified by G2

What do you like best?
With TensorFlow you can do pretty much anything you want in the broad area of machine learning. But the amazing thing about it is that you can also use this software to deal with math problems outside of machine learning. The use of computation graphs along with TensorBoard makes model visualization very intuitive.
What do you dislike?
The learning curve can be quite steep if, like me, you start with no knowledge of TensorFlow's computational model philosophy. Once you get the hang of things, it can be quite rewarding.
Also, as the software is updated quite frequently, it seems the documentation is not as accurate as it could be, leading to quite a few headaches.
What problems are you solving with the product? What benefits have you realized?
I am working with time series forecasting models, and TensorFlow allows for pretty fast prototyping while allowing a huge degree of freedom in terms of model features that you can incorporate.
Recommendations to others considering the product:
Be VERY patient. It may look a bit overwhelming, but it is an amazing tool to master and a great philosophy of computation to understand and follow.


    Information Technology and Services

Deep learning made easy with TensorFlow

  • October 07, 2017
  • Review verified by G2

What do you like best?
The ease of building deep learning models and the high level API's. One can build all kind of architectures pertaining to deep learning. One can use it to build models to solve computer vision problems, perform speech analytics, text analytics, seq2seq models. It supports major algorithms such as ConvNets, RNN, LSTMs, Seq2Seq, It also includes some of the pretrained models which can be customized and trained with new datasets. It can also scale to use multi -gpu systems out of the box without much configuration.
What do you dislike?
Some of the benchmark results show it doesn't train fast, I hope the team is working on making it faster. Also it doesn't include other ML models for comparison.
What problems are you solving with the product? What benefits have you realized?
We are trying to use multi-layer neural network in customer analytics space. We use various models and TensorFlow is easy to implement using high level api.

Personally, I have used it in building forecasting models, sentiment analysis, text analytics, language translation, general adversarial networks. The implementation for all the models were very easy and it provided great benefits in terms of using multi-gpu systems efficiently.
Recommendations to others considering the product:
It has good documentation, and there are many online courses to train the resources. There are new features being added regularly and has great flexibility in terms of use common API's and if needed one can make use of computational graphs to build and customize the models as per the need.