2020-11-11T18:17:00Z

What do you like most about TensorFlow?

Julia Miller - PeerSpot reviewer
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PeerSpot user
16

16 Answers

Ashish Upadhyay - PeerSpot reviewer
Real User
Top 5Leaderboard
2023-11-06T14:18:35Z
Nov 6, 2023

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

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Noman Rafique - PeerSpot reviewer
Real User
Top 5
2023-09-22T19:27:00Z
Sep 22, 2023

It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it.

Dan Bryant - PeerSpot reviewer
Real User
Top 20
2023-08-16T18:28:36Z
Aug 16, 2023

TensorFlow provides Insights into both data and machine learning strategies.

Jan-Kees Buenen - PeerSpot reviewer
Real User
Top 10
2023-07-14T09:40:00Z
Jul 14, 2023

What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device.

Reda Bearbia - PeerSpot reviewer
Real User
Top 10
2023-02-21T16:32:00Z
Feb 21, 2023

I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that.

RichardXu - PeerSpot reviewer
Real User
Top 10
2022-08-04T20:54:14Z
Aug 4, 2022

The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market.

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ZM
Real User
2021-07-09T16:43:00Z
Jul 9, 2021

Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers.

PF
Real User
2021-03-29T23:53:31Z
Mar 29, 2021

It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models.

SS
Consultant
2021-03-05T07:13:07Z
Mar 5, 2021

It's got quite a big community, which is useful.

GY
Real User
2020-12-24T21:53:40Z
Dec 24, 2020

Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful.

GB
Real User
2020-12-07T16:25:36Z
Dec 7, 2020

Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training.

JM
Real User
2020-11-29T19:59:00Z
Nov 29, 2020

TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features.

AI
Real User
2020-11-29T05:25:08Z
Nov 29, 2020

The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing.

HL
Real User
2020-11-24T18:35:05Z
Nov 24, 2020

TensorFlow is a framework that makes it really easy to use for deep learning.

JB
Real User
2020-11-17T17:12:00Z
Nov 17, 2020

It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe.

BI
Real User
2020-11-11T18:17:00Z
Nov 11, 2020

Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment.

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is...
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