What is TensorFlow?

TensorFlow is a popular framework of machine learning and deep learning. It is a free and open-source library which is released on 9 November 2015 and developed by Google Brain Team. It is entirely based on Python programming language and use for numerical computation and data flow, which makes machine learning faster and easier.

TensorFlow can train and run deep neural networks for image recognition, handwritten digit classification, recurrent neural network, word embedding, natural language processing, video detection, and many more. TensorFlow is run on multiple CPUs or GPUs and also mobile operating systems.

The word TensorFlow is made by two words, i.e., Tensor and Flow

Tensor is a multidimensional array
Flow is used to define the flow of data in operation.

Prerequisite

TensorFlow is completely based on Python. So, it is essential to have basic knowledge of Python. A good understanding of basic mathematics and artificial intelligence concept allows us to understand TensorFlow easily.

History of TensorFlow

Many years ago, deep learning started to exceed all other machine learning algorithms when giving extensive data. Google has seen it could use these deep neural networks to upgrade its services:
Google search engine
Gmail
Photo
They build a framework called TensorFlow to permit researchers and developers to work together in an AI model. Once it is approved and scaled, it allows lots of people to use it.
It was first released in 2015, while the first stable version was coming in 2017. It is an open-source platform under Apache Open Source License. We can use it, modify it, and reorganize the revised version for free without paying anything to Google.

Components of TensorFlow

Tensor

The name TensorFlow is derived from its core framework, "Tensor." A tensor is a vector or a matrix of n-dimensional that represents all types of data. All values in a tensor hold similar data types with a known shape. The shape of the data is the dimension of the matrix or an array.
A tensor can be generated from the input data or the result of a computation. In TensorFlow, all operations are conducted inside a graph. The group is a set of calculations that takes place successively. Each transaction is called an op node are connected.

Graphs

TensorFlow makes use of a graph framework. The chart gathers and describes all the computations done during the training.

Advantages

It was fixed to run on multiple CPUs or GPUs and mobile operating systems.
The portability of the graph allows to conserve the computations for current or later use. The graph can be saved because it can be executed in the future.
All the computation in the graph is done by connecting tensors together.
Consider the following expression a= (b+c)*(c+2)

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