### TensorFlow- Most Famous Deep Learning Library

- Amruta Bhaskar
- Oct 25, 2019
- 0 comment(s)

Deep learning may be a subfield of machine learning that's a group of algorithms that are stimulated by the structure and performance of the brain.

TensorFlow is the second machine learning framework that Google created, build, and train deep learning models. You’ll be able to use the TensorFlow library does numerical computations that in itself doesn’t appear too special.

**Introducing Tensors**

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To understand tensors well, it’s important to own some operating data of algebra and vector calculus. You already browse within the introduction that tensors square measure enforced in TensorFlow as two-dimensional information arrays. However, some of the introduction is probably required so as to fully grasp tensors and their use in machine learning.

**Plane Vectors**

Before you move into plane vectors, it’s a decent plan to shortly revise the idea of “vectors”; Vectors square measures special sorts of matrices, that square measures rectangular arrays of numbers. As a result of vectors square measure ordered collections of numbers, they're typically seen as column matrices: they need only 1 column and an explicit range of rows. In alternative terms, you'll conjointly think about vectors as scalar magnitudes that are given a direction.

**Remember:**

An example of a scalar is “5 meters” or “60 m/sec”, whereas a vector is, for instance, “5 meters north” or “60 m/sec East”. The distinction between these 2 is clearly that the vector incorporates a direction. Yet, these examples that you simply have seen up as yet might sound distant from the vectors that you simply would possibly encounter once you’re operating with machine learning issues. This is often normal. The length of a mathematical vector may be a pure number: it's absolute. The direction, on the opposite hand, is relative. It's measured relative to some reference direction and has units of radians or degrees. You always assume that the direction is positive and in gyration from the reference direction.

Visually, of course, you represent vectors as arrows. This suggests that you simply will think about vectors conjointly as arrows that have direction and length. The direction is indicated by the arrow’s head, whereas the length is indicated by the length of the arrow.

**So what concerning plane vectors then?**

Plane vectors square measure the foremost easy setup of tensors. They’re very like regular vectors as you've got seen on top of, with the only real distinction that they notice themselves during a vector house.

To grasp this higher, let’s begin with an example: you've got a vector that's two X one. This suggests that the vector belongs to the set of real numbers that come back paired 2 at a time. Expressed otherwise, they're a part of two-space. In such cases, you'll be able to represent vectors on the coordinate (x,y) plane with arrows or rays.

Working from this coordinate plane during a normal position wherever vectors have their end at the origin (0,0), you'll be able to derive the x coordinate by observing the primary row of the vector, whereas you’ll notice the y coordinate within the second row. Of course, this normal position continually ought not to be maintained. Vectors will move parallel to themselves within the plane while not experiencing changes.

Note that equally, for vectors that square measure of size three X one, you bring up the three-space. You'll be able to represent the vector as a figure with arrows inform to positions within the vectors pace. They're drawn on the quality x, y and z axes.

It’s nice to own these vectors and to represent them on the coordinate plane. However, in essence, you've got these vectors so you'll be able to perform operations on them and one issue that may assist you in doing this is often by expressing your vectors as bases or unit vectors.

Unit vectors square measure vectors with a magnitude of 1. You’ll typically acknowledge the unit vector by a microscopic letter with a diacritic, or “hat”. Unit vectors can are available in convenient if you would like to precise a 2-D or 3-D vector as a total of 2 or 3 orthogonal elements, like the x− and y−axes, or the z−axis.

And after you square measure talking concerning expressing one vector, for instance, as sums of elements, you’ll see that you’re talking concerning element vectors, that square measure 2 or a lot of vectors whose total is that given vector.

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**Tensors**

Next to plane vectors, conjointly convectors and linear operators square measure 2 alternative cases that each one 3 along have one issue in common: they're specific cases of tensors. You continue to keep in mind however a vector was characterised within the previous section as scalar magnitudes that are given a direction.

A tensor, then, is that the mathematical illustration of a physical entity that will be characterised by magnitude and multiple directions.

Author: Ravinder Joshi