11 Reason Why TensorFlow is So Popular
Malini Shukla
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TensorFlow Features | Why TensorFlow Is So Popular
TensorFlow gives us an interactive multiplatform programming interface which is scalable and much stable when compared to other deep learning libraries available, which are still very experimental.
So, let’s start TensorFlow Features.
Features of Tensorflow
Below, we are discussing some important TensorFlow Features:
a. Responsive Construct
With TensorFlow we can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit.
b. Flexible
One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, it offers you that option.
Do you know about TensorFlow Linear Model
c. Easily Trainable
It is easily trainable on CPU as well as GPU for distributed computing.
d. Parallel Neural Network Training
TensorFlow offers pipelining in the sense that you can train multiple neural networks and multiple GPUs which makes the models very efficient on large-scale systems.
Features of Tensorflow
Below, we are discussing some important TensorFlow Features:
a. Responsive Construct
With TensorFlow we can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit.
b. Flexible
One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, it offers you that option.
Do you know about TensorFlow Linear Model
c. Easily Trainable
It is easily trainable on CPU as well as GPU for distributed computing.
d. Parallel Neural Network Training
TensorFlow offers pipelining in the sense that you can train multiple neural networks and multiple GPUs which makes the models very efficient on large-scale systems.
e. Large Community
Needless to say, if it has been developed by Google, there already is a large team of software engineers who work on stability improvements continuously.
f. Open Source
- The best thing about this machine learning library is that it is open source so anyone can use it as long as they have internet connectivity.
- So, people manipulate the library in ways unimaginable and come up with an amazing variety of useful products, it has become another DIY community which has a huge forum for people getting started with it and for those who find it hard to use it or to get help with their work.
Read about Distributed TensorFlow
. Feature Columns
- Tensorflow has feature columns that could be thought of as intermediaries between raw data and estimators, therefore, bridging input data with your model.