TensorFlow Reviews
(Rated by 14 users)
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- Tops: $23 - $70
- Bottoms: $27 - $70
- Outerwear: $34 - $70
- Kids: $29 - $75
Overall Rating
4.6
Base on 14 Reviews
Ratings by Feature
Ratings by Feature
- Price & Quality4.3
- Good Value4.3
- Customer Service4.7
Recent Customer Reviews (14)
Albertine Varieur
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Joshua Parsons
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Birgit Kelley
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Carroll Fisher
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Suzanne Corliss
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Robert Rose
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Steinar Gylfason
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Gracie Quinn
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Phillip Taylor
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Sylvius Villalobos
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TensorFlow Pricing
AWS instance types
$0.025 - $0.371
EBS General Purpose SSD (gp2) volumes
$0.10
TensorFlow Pros & Cons
Pros
1
Scalability: TensorFlow is not limited to one specific device. It works efficiently on a cellular device as well as on any other complex machine.
2
Open Source Platform: It is available free of cost to anyone who wants to work with it.
3
Graphs: TensorFlow has better data visualization power than any other available library.
4
Debugging: TensorFlow has TensorBoard which allows easy debugging of nodes.
5
Parallelism: TensorFlow employs GPU and CPU systems for its functioning.
6
Compatibility: It is compatible with many programming languages like Python, C++, JavaScript, etc.
7
Architectural Support: The TensorFlow architecture uses TPU which makes computation faster than CPU and GPU.
8
Library Management: Being backed by Google, TensorFlow is updated frequently and is capable of displaying outstanding performance.
9
Comprehensive Library: TensorFlow provides a comprehensive library of functions and classes to build and train machine learning models from scratch.
10
High-Level APIs: TensorFlow offers high-level APIs like tf.keras for building and training models with ease.
11
Ecosystem of Tools and Libraries: TensorFlow supports a variety of tools and libraries such as TensorFlow Lite, TensorFlow.js, tf.data, and TFX for different use cases.
12
Pretrained Models and Datasets: TensorFlow.org provides access to pretrained models and ready-to-use datasets for various use cases, which can significantly reduce the time and effort required to train a model.
13
TensorBoard: This tool allows users to visualize and track the development of machine learning models, making it easier to understand, debug, and optimize them.
14
Scalability and Flexibility: TensorFlow can run on a variety of hardware platforms, from mobile devices to large-scale distributed systems. This makes it a flexible solution for different machine learning tasks.
15
Community Support: Being an open-source platform, TensorFlow has a large community of users and contributors who can provide support and share their knowledge and experiences.
CONS
1
No Windows Support: TensorFlow has a very limited set of features for Windows users.
2
Slow: It is comparatively slower and less usable compared to its competing frameworks.
3
GPU Support: TensorFlow has only NVIDIA support for GPU and Python programming language support for GPU programming.
4
Frequent Updates: TensorFlow undergoes frequent updates making it overhead for a user to time to time uninstall and reinstall it.
5
Architectural Limitation: TensorFlow’s TPU architecture allows only execution of models and doesn’t allow its training.
TensorFlow Features and Benefits
Features
Scalability
TensorFlow is not limited to one specific device. It works efficiently on a cellular device as well as on any other complex machine.
Open Source Platform
It is available free of cost to anyone who wants to work with it.
Graphs
TensorFlow has better data visualization power than any other available library.
Debugging
TensorFlow has TensorBoard which allows easy debugging of nodes.
Parallelism
TensorFlow employs GPU and CPU systems for its functioning.
Compatibility
It is compatible with many programming languages like Python, C++, JavaScript, etc.
Architectural Support
The TensorFlow architecture uses TPU which makes computation faster than CPU and GPU.
Library Management
Being backed by Google, TensorFlow is updated frequently and is capable of displaying outstanding performance.
Comprehensive Library
TensorFlow provides a comprehensive library of functions and classes to build and train machine learning models from scratch.
High-Level APIs
TensorFlow offers high-level APIs like tf.keras for building and training models with ease.
Ecosystem of Tools and Libraries
TensorFlow supports a variety of tools and libraries such as TensorFlow Lite, TensorFlow.js, tf.data, and TFX for different use cases.
Pretrained Models and Datasets
TensorFlow.org provides access to pretrained models and ready-to-use datasets for various use cases, which can significantly reduce the time and effort required to train a model.
TensorBoard
This tool allows users to visualize and track the development of machine learning models, making it easier to understand, debug, and optimize them.
Scalability and Flexibility
TensorFlow can run on a variety of hardware platforms, from mobile devices to large-scale distributed systems. This makes it a flexible solution for different machine learning tasks.
Community Support
Being an open-source platform, TensorFlow has a large community of users and contributors who can provide support and share their knowledge and experiences.