TensorFlow Reviews & Insights

Score8.1 out of 10

55 Reviews and Ratings

Community insights

TrustRadius Insights for TensorFlow are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Clear Documentation: Many users have found the documentation for multi-GPU support in TensorFlow to be simple and clear. This has been helpful for users who are new to working with multiple GPUs, as it allows them to easily understand and implement this feature.

Powerful Visualization Tools: Reviewers appreciate the ability to visualize the graph using TensorBoard, as it helps them understand and navigate through complex models. The interactive nature of TensorBoard also allows users to log events and monitor output over time, providing a convenient way to perform quick sanity checks.

Active Community Support: Users highly value the active community surrounding TensorFlow, which has helped them learn faster and overcome obstacles in their development work. The availability of readily available answers and top-notch documentation from the community has been instrumental in ensuring a smooth experience while working with TensorFlow.

TensorFlow Reviews

3 Reviews

Small Businesses (1-50 employees)

Predict with confidence : Tensorflow

Rating: 8 out of 10

Incentivized

April 12, 2019

Use Cases and Deployment Scope

We use TensorFlow for machine learning implementations. Primarily for predictive analysis and recommendation engines. It is being used at an organization level. Our objective is to use a large amount of publicly available data and make meaningful insights from it. It has helped us make better predictions and save costs.
We also use it for time series analysis to make predictions in the equity market. TensorFlow has been a powerful and easy to deploy tool for various algorithms.

Pros

Support for many libraries and programming languages.
Ability to use GPU and TPU - hence faster execution.
Low effort in getting started in development, hence ease of learning.

Cons

Graphic interface to create layers can help beginners.
Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don't focus on that.
Better support to integrate with files on the cloud.

Likelihood to Recommend

Best suited for deployment on the cloud with the subscription-based model for execution infrastructure. For startups or for companies that do not have a strong data science staff, learning Tensorflow is easy because of the libraries and online tutorials availability.

It can be avoided when your development stack is Microsoft, as using Azure may provide better integration. Also, if the work requires detailed customization of the algorithm, it may be easier to work directly with Python code and TensorFlow may not help.

Anupam Mittal

Founder in Information Technology at Brontominds (Information Technology and Services, 1-10 employees)

Vetted Review

2 years of experience

View profile

Best deep learning tool

Rating: 9 out of 10

Incentivized

October 30, 2018

Use Cases and Deployment Scope

I have used this tool for building a recommendation system. We have built a system to recommend flights to users so we used TensorFlow to build that. I personally have used this tool in a different sector like doing image processing, building an image recognition algorithm and implementing neural networks etc. This is being used by a part of our company not by the whole or across the company.

Pros

TensorFlow is very powerful to build the neural networks, it gives you the power to write your own implementation with full customisation.
It has inbuilt methods which helps a lot when it comes to writing your own implementation of neural networks
It has lots of inbuilt methods to do the data processing like reading data from a directory as classes using an image data generator etc.

Cons

First and biggest con is that it has a very tense learning curve. Understanding the concept of a tensor, a dynamic graph is difficult and takes a lot of time to learn
As compared to Keras, TensorFlow takes a lot of time to build and implement a neural network. You have to write everything by yourself.

Likelihood to Recommend

There are lots of use cases to use this tool and also lot of cases where you should look to this. Like when it comes to building or playing with a deep learning algorithm like neural networks you should choose this one. But if you are implementing some other machine learning algorithm then definitely you have to check whether TensorFlow is a good choice or not. And you can also use this for NLP as well.

Ajay Shewale

Co-founder | Data Scientist at Blubyn in Engineering at Blubyn (Information Technology and Services, 1-10 employees)

Vetted Review

2 years of experience

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My perception of the first year with TensorFlow

Rating: 7 out of 10

Incentivized

September 25, 2018

Use Cases and Deployment Scope

Currently, we use machine-learning models to develop solutions for our clients. But sometimes the usual models (decision tree, naive Bayes, random forest) are not helping us to find a suitable model, or it generates too many levels of modeling. Sometimes we use the pre-build neural networks included in some libraries. We are not yet experts in TensorFlow, but using Keras, it helped us to arrive to predictive models in a shorter time and with more accuracy.

Pros

Modeling for complex problems with large amounts of data
Modeling when the client is not interested in building the model patiently in levels
Guiding what we are doing wrong with other models

Cons

Too many lines of code for some actions
Not very intuitive for non-programming engineers

Likelihood to Recommend

It is better when there is a lot of data available and the complexity of variables is high—for instance, when nobody has modeled that problem before. If there is not enough data, it does not work, or if it works, it is not going to help to model reality. It is also very good to test the performance of any other model even if the TensorFlow model itself is not going to be the solution for the client.

Jose Machicao, MSc

Management Consultant in Engineering at GestioDinamica EIRL (Management Consulting, 1-10 employees)

Vetted Review

1 year of experience

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