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Advantage and Disadvantage of TensorFlow

TensorFlow is an open-source machine learning concept which is designed and developed by Google. It offers a very high level and abstract approach to organizing low-level numerical programming. And supporting libraries that can allow our software to run without changes on regular CPU.

It supported platforms include Linux, macOS, Windows, and Android.

TensorFlow models can also be run without a traditional computer platform in the Google Cloud Machine Learning Engine.

Advantage and Disadvantage of TensorFlow

The more advanced technology, and the more useful it can be, but everything has its downside and also this machine learning library. When comparing TensorFlow with other libraries like Torch, SciKit, Theano, Neon, there are drawbacks in several features that the library lets us manipulate. This library is designed and updated by Google, so needless to say, and it has come a far way since its initial release.

Advantages of TensorFlow

Advantage and Disadvantage of TensorFlow

1) Graphs:

TensorFlow has better computational graph visualizations. Which are inherent when compared to other libraries like Torch and Theano.

Advantage and Disadvantage of TensorFlow

2) Library management:

Google backs it. And has the advantages of seamless performance, quick updates, and frequent new releases with new features.

3) Debugging:

It helps us execute subpart of a graph which gives it an upper hand as we can introduce and retrieve discrete data

4) Scalability:

The libraries are deployed on a hardware machine, which is a cellular device to the computer with a complex setup.

5) Pipelining:

TensorFlow is designed to use various backend software (GPUs, ASIC), etc. and also highly parallel.

6) It has a unique approach that allows monitoring the training progress of our models and tracking several metrics.

7) TensorFlow has excellent community support.

8) Its performance is high and matching the best in the industry.

Disadvantages of TensorFlow

Advantage and Disadvantage of TensorFlow

1) Missing Symbolic loops:

When we say about the variable-length sequence, the feature is more required. Unfortunately, TensorFlow does not offer functionality, but finite folding is the right solution to it.

2) No supports for windows:

There is a wide variety of users who are comfortable in a window environment rather than Linux, and TensorFlow doesn't satisfy these users. But we need not worry about that if we are a window user we can also install it through conda or python package library (pip).

3) Benchmark tests:

TensorFlow lacks in both speed and usage when it is compared to its competitors.

4) No GPU support for Nvidia and only language support:

Currently, the single supported GPUs are NVIDIA and the only full language support of Python, which makes it a drawback as there is a hike of other languages in deep learning as well as the Lau.

5) Computation Speed:

This is a field where TF is delaying behind, but we focus on the production environment ratherish than the performance, it is still the right choice.

6) No support for OpenCL.

7) It requires fundamental knowledge of advanced calculus and linear algebra along with a good understanding of machine learning also.

8) TensorFlow has a unique structure, so it's hard to find an error and difficult to debug.

9) There is no need for any super low-level matter.

10) It is a very low level with a steep learning curve.






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