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TensorFlow 1.6 Python 3.6 CPU Production

Jetware | 180306-tensorflow_1_6_0-python_3_6_3

Linux/Unix, Amazon Linux 2017.09.1 - 64-bit Amazon Machine Image (AMI)

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External reviews

56 reviews
from G2

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


    Kushal P.

Like why would you use another ML platform

  • December 23, 2021
  • Review provided by G2

What do you like best?
Python based and API is intuitive. Keras is great and uses the Tensorflow library. I used scikit-learn prior and it was so much harder to understand and require way more code to get the same things done. The user-friendly interface is honestly the best part of Tensorflow/Keras.
What do you dislike?
Not a lot, but for Tensorflow Lite, a user manual to port to other boards would be great. I wanted to use Tensorflow Lite on my TM4C123GXL board, but it's not a supported platform. I am sure there is a way to get it running on any board, I just do not know how.
What problems are you solving with the product? What benefits have you realized?
Mainly educational purposes. I wanted to create a fire detection program that I hoped could be used to combat wildfires. I haven't really gotten the time to do this, but I still want to do it.
Recommendations to others considering the product:
Look no further, this is the ML platform to use.


    Hiteshi Jain .

Tensorflow review

  • December 23, 2021
  • Review provided by G2

What do you like best?
Tensorflow is very mature deep learning library which is heavily used in production scenarios. I particularly like the tensorflow-lite version which comes along which reduces the size of the model and is good to deploy in edge devices.
What do you dislike?
It requires a little more coding as compared to pytorch. Pytorch is more pythonic and hence is easier to learn and implement
What problems are you solving with the product? What benefits have you realized?
For deep learning model development for the industry I am working in


    Semiconductors

Tensorflow ML platform

  • December 01, 2021
  • Review provided by G2

What do you like best?
Very powerful platform with Keras and other ml/dl libraries
TFRecord is very efficient way of handling/storing data
What do you dislike?
Very heavy software for inferencing though TFLite is good for mobile
What problems are you solving with the product? What benefits have you realized?
Training ML models for computer vision, natural language processing and graph neural networks.


    Alex M.

Most mathematically-oriented ML framework

  • November 30, 2021
  • Review verified by G2

What do you like best?
For people who grew up learning the math of backprop, who enjoy thinking about syntax trees and computation graphs, Tensorflow will allow you to make full use of you that insight. Interesting loss functions like Wasserstein loss (where the gradient itself enters as part of the loss function) enter naturally.
What do you dislike?
The mix between Tensorflow v1 and v2 code is somewhat difficult to learn, if you only get into it now. Tensorflow v2 is modeled much more on Keras, and is designed for you to particular architectures and pipelines. That's great, but if you then want to mix that with the flexibility of v1, you run into a lot of pain.
What problems are you solving with the product? What benefits have you realized?
I've used Tensorflow as a black-box optimizer for searching for Quantum Error-Correcting Codes. That probably doesn't sound like Machine Learning, right? But it's gradient descent on large parallel datasets, so hey, it works. I've also seen it for e.g. physics simulations, card game simulation, a wide variety of "parallel" tasks. In the most liberal interpretation, Tensorflow is "CUDA but better": a way to use your GPU for parallel tasks in a general setting.


    Information Technology and Services

Graphical computation in deep learning

  • November 25, 2021
  • Review provided by G2

What do you like best?
The fact is that you can create the network and then do computation all at once. The computation is well optimized to run on GPU. The tensorboard support enables us to view the metric like accuracy and weights during the training which is absent in other deep learning packages
What do you dislike?
The high level api is not present in the package itself. For that we need to use keras or other packages which is build on top of this but these high level API is not native to tensorflow
What problems are you solving with the product? What benefits have you realized?
Building and training deep neural networks. Getting deep into the training process and visualizing it does makes a lot of difference in getting very accurate models.


    KanuPriya K.

TensorFlow for AI Model Development

  • November 10, 2021
  • Review verified by G2

What do you like best?
The most valuable part of TensorFlow is the Tensorboard. While training the AI model development, it provides better visualization for debugging and error handling.
What do you dislike?
The least liked part of TensorFlow is it's implementation speed. In comparison to another deep learning framework, development time is higher in TensorFlow
What problems are you solving with the product? What benefits have you realized?
I am using TensorFlow Framework for complex Neural Network Implementations and Face Recognition deep learning model development.


    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

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.