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External reviews

43 reviews
from G2

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


    Lucifer M.

Write Neural Networks in under 50 lines

  • November 29, 2018
  • Review provided by G2

What do you like best?
- Tensorflow is one of the best frameworks to do deep learning, machine learning for huge datasets.
- The dataset can have millions of records can be of terabytes in size
What do you dislike?
- Tensorflow can be slow when working with large files. A GPU might be required to help with the processing
What problems are you solving with the product? What benefits have you realized?
I created a neural network on CDC sources datasets to run predictions. Once I worked on many diseases and created individual model, I developed a predictive model platform to run several neural networks at once for multiple diseases.


    Semiconductors

Great library for performing deep learning

  • November 29, 2018
  • Review provided by G2

What do you like best?
Great documentation written by Google's Tensorflow Team, and a lot of community engagement. Most answers regarding Tensorflow have answers available from the community.
What do you dislike?
Library interface is a bit lower-level compared to other Python interfaces like Keras, but developer experience has been improving.
What problems are you solving with the product? What benefits have you realized?
Performing supervised learning on business problems requiring predictions. We have realized cost savings using the results of our predictions.
Recommendations to others considering the product:
Useful for deep learning.


    james t.

very intuitive and powerful platform.

  • November 29, 2018
  • Review provided by G2

What do you like best?
Offers great platform to develop in the AI domain to deliver business value efficiently.
What do you dislike?
None at this time, as we are still in POC.
What problems are you solving with the product? What benefits have you realized?
AI proof of concept in the legal and compliance space to help formulate AI strategy.


    Banjo O.

TensorFlow Review

  • November 29, 2018
  • Review provided by G2

What do you like best?
Lots of things you can do for creating models
What do you dislike?
Very complex, has there are alot of manual steps
What problems are you solving with the product? What benefits have you realized?
Machine Learning in the cloud


    Biotechnology

applicability of tensor flow for research purposes

  • November 27, 2018
  • Review provided by G2

What do you like best?
the models covered by tensor flow are great for research purboses and the examples provided are good
What do you dislike?
more examples which covers several research fields could be covered
What problems are you solving with the product? What benefits have you realized?
researcgh at university related to prediciting students behaviour


    Research

fast, reliable, and amazing machine learning library

  • November 09, 2018
  • Review verified by G2

What do you like best?
It covers a wide range of Machine learning problems, superviesd, unsupervised, reinforcment ... learning
very fast possible to run in parallet
What do you dislike?
for beginners it can be very confusing and they can easily stuck in the different pages of official tutorial
I beleive the toturial could use some introductory videos
What problems are you solving with the product? What benefits have you realized?
machine learning application in engineering problems,
we solve energy engineering problems with the help of ML using Tensorflow


    Ahmad A.

Amazing library if you are expert in machine learning,

  • November 04, 2018
  • Review provided by G2

What do you like best?
It is amazingly fast, It works in parallel, and supports GPU
What do you dislike?
The idea of Tensors is not very well explained in the official website, and that makes the user to panic if they do not understand the most basic idea of it after an hour of digging
What problems are you solving with the product? What benefits have you realized?
I am a researcher and an engineer, I combine my Machine learning knowledge with the use of TensorFlow to apply it to real life engineering applications
Recommendations to others considering the product:
Do not get disapointed if you are difficulties mastering this library,
once you know how to correctly use it, the creating fantastic machine learning models will be fun and easy


    Andrew C.

TensorFlow Worth the Learning Curve

  • October 12, 2018
  • Review verified by G2

What do you like best?
I like how easy TensorFlow makes building ML models without sacrificing low-level implementation capabilities. It includes a wide variety of prebuilt models and model subblocks that can be plugged together using simple python scripts. Tensorflow handles the implementation details seamlessly allows you to abstract away the underlying hardware, be they GPU's, CPU's or TPU's. We don't have to think about what kind of convolution algorithm we're using unless we absolutely want to. The data ingestion pipeline makes handling hundreds of GB of data a simple task. No more loading everything into RAM or worrying about file access and formatting. It does come with a price and it isn't as intuitive as it could be but it is well worth learning if you are serious about applied machine learning or just experimenting.
What do you dislike?
I dislike the define-and-run model of TensowFlow. It is unintuitive and occasionally lends itself to clunky solutions. It differs from the define-by-run model of the other major ML frameworks which is a barrier to access for many. I also dislike the structure of variables as tensors. It is often unclear whether your variables need to be tensors or plain python types. Once you get the hang of using TensorFlow it becomes obvious but something as simple as variable definitions shouldn't be so opaque.
What problems are you solving with the product? What benefits have you realized?
We are building models using Tensorflow that can learn from our datasets to accurately classify samples. Previously, building these models required highly domain specific knowledge were built in an ad hoc way for each class of data. Tensorflow allows us to build one model that can be far more easily adapted and changed.
Recommendations to others considering the product:
It is worth the learning curve. Google has a fantastic introductory series on both Machine Learning and TensorFlw specifically that I highly recommend.


    Farming

best one out there

  • October 06, 2018
  • Review provided by G2

What do you like best?
the Tensor flow API is the best and the model created on the desktop can be used any where.
And the availability of pre trained models is anothe rbest part.
What do you dislike?
Little hard for the non coding person to train and create models.
What problems are you solving with the product? What benefits have you realized?
ML and predictions
Recommendations to others considering the product:
Should develop a UI for deploying new models and training them.


    Computer Software

A powerful deep learning library, with certain rough edges

  • July 25, 2018
  • Review provided by G2

What do you like best?
Automatic differentiation and support for backpropagation through many useful mathematical operations. Tensorboard interface for monitoring and visualisation.
What do you dislike?
The programming model is somewhat cumbersome, and reliant on global state behind the scenes. For any task there seems to be multiple incompatible ways of achieving it, with varying degrees of documentation. The API is a mess, with many different high level interfaces. There is no standardised workflow, which makes mixing and matching models from different sources very difficult.
What problems are you solving with the product? What benefits have you realized?
Training deep learning models for video analysis. After the initial hurdles, it does the job.
Recommendations to others considering the product:
Carefully consider the alternatives, such as PyTorch which can be easier for development by specifically targeting Python and the Pythonic way of programming (although potentially at the expense of flexibility).