Why Is Tensorflow the Most Preferred Library in Deep Learning?

Data Science, AI and ML
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#1

Why Is Tensorflow the Most Preferred Library in Deep Learning?

#2

The key reason is TensorFlow’s high-level APIs making deep learning accessible to everyone. TensorFlow provides pre-built functions and advanced operations to ease the task of building different neural network models. It provides the required infrastructure and hardware which makes them one of the leading libraries used extensively by researchers and students in the deep learning domain.

In addition to research tools, TensorFlow extends the services by bringing the model in production using TensorFlow Serving. It is specifically designed for production environments, which provides a flexible, high-performance serving system for machine learning models. It provides all the functionalities and operations which makes it easy to deploy new algorithms and experiments as per changing requirements and preferences. It provides an excellent feature of out-of-the-box integration with TensorFlow models which can be easily extended to serve other types of models and data.

TensorFlow’s API is a complete package which is easier to use and read, plus provides helpful operators, debugging and monitoring tools, and deployment features. This has led to growing use of TensorFlow library as a complete package within the ecosystem by the emerging body of students, researchers, developers, production engineers from various fields who are gravitating towards artificial intelligence.
ref:https://hub.packtpub.com/tensorflow-always-tops-machine-learning-artificial-intelligence-tool-surveys/#:~:text=TensorFlow%20provides%20pre-built%20functions,in%20the%20deep%20learning%20domain.

#3

a. Responsive Construct-

You can easily visualize each & every part of the graph which is not an option while using Numpy or SciKit.

b. Flexible-

It is flexible in its operability. It has modularity and the parts of it which you want to make standalone, it offers you that option.

c. Easily Trainable-

Easily trainable on CPU as well as GPU for distributed computing.

d. Parallel Neural Network Training-

You can train multiple neural networks & multiple GPUs, which makes the models very efficient on large scale systems.

e. Large Community-

It has been developed by GOOGLE, there is a large team of software engineers, who work on stability improvements continuously.

f. Open Source-

It is an open source so anyone can use it as long as they have internet connectivity.

g. Feature Columns-

Features columns that could be thought of as intermediaries between raw data & estimators.

h. Availability of Statistical Distributions- The library provides distribution function, it includes Bernoulli, Chi2, Uniform, Gamma, Beta, they are important especially while considering probabilistics approaches such as Bayesian models.

i. Layered Components-

Produce layered operations of weights & biases and also provide batch normalization, dropout layer, convolution layer, etc.

j. Visualizer (with TensorBoard)-

You can inspect a different representation of a model & make the changed necessary while debugging it.