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TensorFlow In Machine Learning

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. 

Use in Different Areas

There are many development options that you can get with TensorFlow and its installation is also quick.

  1. Abstraction - As in other fields of Artificial Intelligence, abstraction plays a key role in learning. This chapter presents the role and impact of abstraction in two much studied paradigms of Machine Learning: Learning from examples and Learning from reinforcement. 

  2. Visualization - TensorFlow includes a visualization tool, which is called the TensorBoard. It is used for analyzing Data Flow Graph and also used to understand machine-learning models. The important feature of TensorBoard includes a view of different types of statistics about the parameters and details of any graph in vertical alignment. The tensor flow allows you to use different types of content formats to learn about the neural networks such as audio, video, histogram and graph.

  3. Community - It support the community so you can get help with us and discussing form it large scale community

  4. Use to develop Application - Tensorflow is used to develop different types of web and mobile application using different programming languages like: Flask, etc.

  5. It support Keras - Kera is the API that is widely used to develop the machine learning models. This is highly efficient. Due to its quick prototyping and research methods, it is widely used in different industries. It supports Kera and is integrated with TensorFlow. It makes it a piece of cake for the developers to use machine learning with the help of TensorFlow. TensorFlow offers many advantages compared to its counterparts to develop machine learning models.

Why we use TensorFlow?

Easy model building:

Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.

Robust ML production anywhere:

Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.

Powerful experimentation for research:

A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.