## Is the dot product a tensor product?

In this particular example, the tensor product is essentially the direct product of two vectors. You can generalize this idea to higher rank tensors straightforwardly. The dot product combines two vectors into a scalar (a number). It is actually the inner product.

**Is a tensor a vector of vectors?**

Tensors are simply mathematical objects that can be used to describe physical properties, just like scalars and vectors. In fact tensors are merely a generalisation of scalars and vectors; a scalar is a zero rank tensor, and a vector is a first rank tensor.

**What is vector and tensor?**

vector are invariant physical properties that are independent of the frame of reference. Tensors. are physical quantities such as stress and strain that have magnitude and two or more directions.

### What is tensor product in quantum computing?

Tensor Products are used to describe systems consisting of multiple subsystems. Each subsystem is described by a vector in a vector space (Hilbert space). For example, let us have two systems I and II with their corresponding Hilbert spaces HI and HII.

**Why is a tensor necessary?**

Tensors have become important in physics because they provide a concise mathematical framework for formulating and solving physics problems in areas such as mechanics (stress, elasticity, fluid mechanics, moment of inertia.), electrodynamics (electromagnetic tensor, Maxwell tensor, permittivity, magnetic …

**Why do we need tensor product?**

Tensor products are useful because of two reasons: they allow you to study certain non linear maps (bilinear maps) by transforming them first into linear ones, to which you can apply linear algebra; they allow you to change the ring over which a module is defined.

#### What is tensor computing?

A tensor is a container which can house data in N dimensions. Often and erroneously used interchangeably with the matrix (which is specifically a 2-dimensional tensor), tensors are generalizations of matrices to N-dimensional space.

**Why are tensors used in machine learning?**

Remember, most machines cannot learn without having any data. And modern data is often multi-dimensional. Tensors can play an important role in ML by encoding multi-dimensional data. For example, a picture is generally represented by three fields: width, height and depth (color).

**Is outer product same as tensor product?**

The outer product of tensors is also referred to as their tensor product, and can be used to define the tensor algebra. The outer product contrasts with: The dot product (also known as the “inner product”), which takes a pair of coordinate vectors as input and produces a scalar.

## What is the difference between dot product and cross product?

The dot product of two vectors is the product of their magnitudes and the cosine of the angle that they subtend on each other.

**Is the dot product the same as an inner product?**

This inner product is often called the dot product. So in this context, inner product and dot product mean the same thing. But inner product is a more general term than dot product, and may refer to other maps in other contexts, so long as they obey the inner product axioms.

**What do Dot and cross vector products actually mean?**

– small angle gives small products – vector product is at right angle to the product of the two vectors – right hand rule is used: A x B = -B x A – think about torque as an application

### What is the meaning of dot product?

Calculating Work (Cross Product for Torque),W=f.d – force and distance are given as vectors.

https://www.youtube.com/watch?v=Rj1SI6kwxR8