Well, Before getting into TensorFlow -we are here to find the maths and science in the build to TensorFlow.

Definitely, it is not a technology just found in 21st century - on good perspective TensorFlow is a evolution progress of maths.

Scalar - a Magnitude not direction.For example: Time,Distance,Energy. This might look very silly explaining it here.But, it defines our view of One Dimensional things in maths.

Vector - has Magnitude and Direction. Example: Increase and Decrease of Temperature. Here the working becomes like a graph of 2 Dimensional representation.

Since, Machine Learning or Deep Learning working with points of dimensions with respect to each data flowing through the process of decision making - it is easily related to Dimensions of graph and extracting the values out of graph will eventually make equations.

Let's look at the 2D graph. For example we can get equation for each point but with the same corresponding quantity of speed(y) and Time(x).

x+y=1

2x+2y=2

Just a equation can be converted into array by removing the co-efficient.

1 1

2 2

This is represented as Array or Matrix - Matrix is the best form used inside Machine Learning or Deep Learning as it has mathematical calculations around it. But, with computational Language it is named as Array.

Now, TensorFlow - A Multidimensional Array or Matrix that can be worked out inside a graph(Data Flow Environment)

As we can see the world with 3 - Dimensions but cannot predict exactly as the happening of many things around us shows different perspectives.

Deep Learning generally works with the pattern of brain as firing neurons and neurons create huge number of dimensions hence tensorflow is just more than our view from eyesight but not more than brain.

Hence - Multidimensional data shows we live and work with more dimensions of life with data and TensorFlow helps in plotting new dimensional lifestyle.

Definitely, it is not a technology just found in 21st century - on good perspective TensorFlow is a evolution progress of maths.

Scalar - a Magnitude not direction.For example: Time,Distance,Energy. This might look very silly explaining it here.But, it defines our view of One Dimensional things in maths.

Vector - has Magnitude and Direction. Example: Increase and Decrease of Temperature. Here the working becomes like a graph of 2 Dimensional representation.

Since, Machine Learning or Deep Learning working with points of dimensions with respect to each data flowing through the process of decision making - it is easily related to Dimensions of graph and extracting the values out of graph will eventually make equations.

Let's look at the 2D graph. For example we can get equation for each point but with the same corresponding quantity of speed(y) and Time(x).

x+y=1

2x+2y=2

Just a equation can be converted into array by removing the co-efficient.

1 1

2 2

This is represented as Array or Matrix - Matrix is the best form used inside Machine Learning or Deep Learning as it has mathematical calculations around it. But, with computational Language it is named as Array.

Now, TensorFlow - A Multidimensional Array or Matrix that can be worked out inside a graph(Data Flow Environment)

As we can see the world with 3 - Dimensions but cannot predict exactly as the happening of many things around us shows different perspectives.

Deep Learning generally works with the pattern of brain as firing neurons and neurons create huge number of dimensions hence tensorflow is just more than our view from eyesight but not more than brain.

Hence - Multidimensional data shows we live and work with more dimensions of life with data and TensorFlow helps in plotting new dimensional lifestyle.

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