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Visual Machine Learning Stack for TensorFlow

Websoft9 | Tensorflow 2.4.1.0 - Amazon Linux 2

Linux/Unix, Amazon Linux 2 - 64-bit Amazon Machine Image (AMI)

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External reviews

51 reviews
from G2

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


    Doolitha S.

Best Platform for Building Deep learning models and Train them

  • September 30, 2021
  • Review verified by G2

What do you like best?
It was easy to get on with Tensorflow compared to other machine learning libraries. There are tons of community support, tutorials, videos, and even pre-build models to learn and get maximum out of it in a short time. And what's more, it's completely free and open-source.
What do you dislike?
There is nothing to say in this section. Tensorflow has it all, and I love it. I haven't faced a serious issue yet, and even If I did, the community is there to solve them happily.
What problems are you solving with the product? What benefits have you realized?
I used Tensorflow for part of my final year research project. It was fun to learn and train the model as for my requirement. In the end, I was able to implement it without issues, and it was a success. I realized the true potential of Tensorflow.
Recommendations to others considering the product:
It's a must-use library for anyone who is into deep learning and model creation and training.


    Information Technology and Services

Framework with ease access of AI ML APIs

  • September 27, 2021
  • Review provided by G2

What do you like best?
Graph visualisation in tensorflow is much better.
Frequent updates as its backed by Google.
What do you dislike?
Tensorflow lacks behind in terms of computation and speed.
It's only supported in NVIDIA GPUs.
What problems are you solving with the product? What benefits have you realized?
Tensorflow helps us to train and deploy deep learning based model with ease.


    Information Technology and Services

Best framework to create AI based solutions

  • September 23, 2021
  • Review provided by G2

What do you like best?
Easy to work with framework to train models. Trained model able to use in any AI solution to support image processing.
What do you dislike?
It nearly impossible to train models in Low config devices.
What problems are you solving with the product? What benefits have you realized?
Train and use model in applications of windows and Mobile applications.


    Information Technology and Services

Awesome framework for use of image processing solutions

  • September 22, 2021
  • Review provided by G2

What do you like best?
Easy to train images and create models which you can use in multiple plateforms like Windows, embedded devices and Mobile applications.
What do you dislike?
Sometime it is hang on medium config system otherwise it is ok.
What problems are you solving with the product? What benefits have you realized?
Trains and use model for image processing solutions.


    deniz y.

Perfect for neural networks

  • September 21, 2021
  • Review provided by G2

What do you like best?
It works wonders when processing image, text and audio data. The documentation is very good and easy to use. With Keras, you can do your deep learning work simply and quickly. Open source. The best software library on the market for deep learning. It's reassuring to have Google behind it. Documentation is being updated. Functional.
What do you dislike?
It's forcing the video card. Detecting and resolving errors is sometimes difficult.
What problems are you solving with the product? What benefits have you realized?
I can easily process my data with the models I have prepared.


    Chandresh M.

Framework for solving Machine Learning Problem

  • September 21, 2021
  • Review verified by G2

What do you like best?
The main important thing I like about Tensorflow is, it is Open Source. Anyone can use it and can create multiple Machine Learning applications. I can also visualize my machine learning model in TensorFlow by using Tensorboard. Tensorflow also supports Keras, so we can easily create ML and CNN models using it. Tensorflow is compatible with many programming languages like Java, Python, C++, Ruby etc.
What do you dislike?
One thing I don't like about Tensorflow is, it gives updates regularly. So it becomes a little bit difficult to install new versions. Because sometimes, whatever application I developed may not be supported on a more recent version of Tensorflow.
What problems are you solving with the product? What benefits have you realized?
I am using Tensorflow for creating Machine Learning and Deep Learning Applications.
Recommendations to others considering the product:
I recommend to the ones who want to develop applications quickly and beginner-friendly.


    Wireless

Best for Neural Network Modelling and Other ML Project

  • September 20, 2021
  • Review provided by G2

What do you like best?
This is having will defined and organized Classes for NN
What do you dislike?
Sometimes dependencies give error during run time
What problems are you solving with the product? What benefits have you realized?
I am doing a project that is based on Deep NN and Reinforcement learning


    Kevin P.

Great framework for production grade model development and deployment

  • August 10, 2021
  • Review verified by G2

What do you like best?
Tensorflow is a mature framework that offers many valuable features such as Keras, Tensorboard, data processing modules, easy-to-implement multiprocessing, integration with HDFS, and more. Tensorflow has a strong community and very robust documentation. Tensorflow has many time-saving features, such as easily integrated pre-trained model layers. The TensorFlow model hub is one of the best I have seen in terms of ease of finding and using pre-trained models. There are many demos and example notebooks that demonstrate how to use complex and straightforward concepts.
What do you dislike?
Tensorflow has gone through many iterations over the past years, so code maintenance has been an issue. In my opinion, eager execution is a preferred method of developing and debugging networks; however, experience with legacy TensorFlow makes the switch more challenging. Since the deep learning research community favors PyTorch over TensorFlow, researchers generally find state-of-art models and new methodologies implemented in PyTorch.
What problems are you solving with the product? What benefits have you realized?
We have developed custom models using the Keras API. We have used pre-trained (EfficientNet) models to solve various batch and real-time modeling needs in tabular and image processing learning paradigms. We are also looking into the TensorFlow decision forest package to keep all model development consistent. We use TensorFlow as both an experimental as well as a real-time production platform.
Recommendations to others considering the product:
Start by looking at examples provided by the developers.
Use TensorBoard


    Computer Software

I used TensorFlow for my some of course projects

  • September 01, 2020
  • Review provided by G2

What do you like best?
It is open-source and can use for different platforms. There are lots of tutorials available that can make help students when that stuck in some part. Also, I really like the visualization tools of that through TensorBoar.
What do you dislike?
I have a windows machine and there is no support for Windows
What problems are you solving with the product? What benefits have you realized?
I used for my text analysis class and machine learning class to solve the class project on time series and CNN
Recommendations to others considering the product:
The best way to use it is through python because there are lots of tutorials for that. Also, using Keras through TensorFlow is also fabulous


    Higher Education

Using Deep Neurals Nets with a few lines of code

  • August 11, 2020
  • Review provided by G2

What do you like best?
The Keras API: it makes building, training, working with DNNs, CNNs very easy. It requires only a very small knowlegde of (python) coding. Line by line one can add layers to the network to construct it, and the built-in optimizers do the training. No need to go through the pain if implementing backprop or optimizations steps on your own.
Also nice: the support for GPUs which really speeds up all computions.
What do you dislike?
The (remants of) older TF versions using weird and sometimes incomprehensible stuff like placeholder, logits, etc.
What problems are you solving with the product? What benefits have you realized?
As of now, used TF mainly as coding environent to actually learn ML, DNNs etc. The nice Keras API really helped me a lot to start my own ML exercise projects.