In 2015 Artificial-Intelligence (AI) researchers at Google came up with TensorFlow, now used in building Machine Learning (ML) software. To answer the question “what is a tensor?”, you need to know a bit about TensorFlow.

TensorFlow helps Google engineers to translate new approaches to artificial intelligence into a practical code, improving services such as search and the accuracy of speech recognition.

**What is a Tensor: Google’s Claim is Worth It**

TensorFlow’s immense popularity is helping Google fight for a bigger share of the estimated $40 billion cloud infrastructure market. The company ranks third, behind Amazon and Microsoft. Data that is derived from multiple sources for AI or ML today is largely unstructured.

It may include images, sound, and textual data. To understand TensorFlow, you must understand “what is a tensor”, first. A tensor may be explained as a generalization of vectors and matrices to potentially higher dimensions.

This post aims at explaining tensor and tensor in a TensorFlow, the part played by tensor in Deep Learning, and how tensor impacts Machine Learning. The post will cover:

- What is a tensor in TensorFlow
- Key TensorFlow concepts
- What is a Tensor in Deep Learning
- Tensor in Machine Learning
- Core Machine Learning concepts
- The relation between Tensor Flow and Machine Learning

**What is a Tensor?**

A Tensor, by mathematical definition, may be defined as simple arrays of numbers, or functions, that may transform according to certain rules under a change of coordinates.

In simpler terms, a Tensor may be defined as a single point or collection of isolated points of space (or space-time). In other words, it may be defined over a continuum of points.

In the latter case, the elements of the tensor are functions of the position and the tensor forms what is called a tensor field. This implies that the tensor is defined at every point within a region of space (or space-time), rather than just at a point, or collection of isolated points.

**Tensor in More Details**

Tensor, by rule, may consist of a single number, usually referred to as a tensor of order zero, or simply a scalar. It may become apparent; a scalar may be thought of as an array of dimension zero (same as the order of the tensor).

A typical example of a scalar would be the mass of a particle or object. An example of a scalar field would be the density of a fluid as a function of position.

Another example of a scalar field would be the value of the gravitational potential energy as a function of position. Note that both of these are single numbers (functions) that vary continuously from point-to-point, thereby defining a scalar field.

The next most complicated tensor is the tensor of order one, also known as a vector. It may be defined as a point or points, or it may vary continuously from point-to-point, thereby defining a vector field.

The ordinary three-dimensional space,i.e., a vector has three components (contains three numbers, or three functions of position).

In four-dimensional space-time, a vector has four components. in general, in an n-dimensional space, a vector (a tensor of order one) has n components.

A vector may be thought of as an array of dimension one. This is because the components of a vector can be visualized as being written in a column or along a line, which is one dimensional.

An example of a vector field is provided by the description of an electric field in space. The electric field at any point requires more than one number to characterize because it has both a magnitude (strength) and it acts along a definite direction. It is something not shared with a scalar, such as mass. Generally, both the magnitude and the direction of the field vary from point-to-point.

**Definition of TensorFlow?**

TensorFlow represents tensors as n-dimensional arrays of base data types. It may be defined as a computational framework for building machine learning models.

It provides a variety of different toolkits for constructing models at your preferred level of abstraction. Usually, lower-level APIs may be used to build models by defining a series of mathematical operations.

Alternatively, higher-level APIs (like tf.estimator) are also used to specify predefined architectures, such as linear regressors or neural networks. TensorFlow was developed by the Google Brain team for internal Google use and was released under the Apache 2.0 open-source license on November 9, 2015.

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**TensorFlow**

TensorFlow, as the name indicates, is a framework to define and run computations involving tensors. A Tensor, therefore, is a generalization of vectors and matrices to potentially higher dimensions.

TensorFlow consists of two components:

1. a graph protocol buffer

2. a runtime that executes the (distributed) graph

These two TensorFlow components are analogous to Python code and the Python interpreter. Just as the Python interpreter is implemented on multiple hardware platforms to run Python code, TensorFlow can run the graph on multiple hardware platforms, including CPU, GPU, and TPU.

**Which API(s) are used for TensorFlow?**

The highest level of abstraction is used to solve the problem. Higher levels of abstraction are easier to use but are also (by design) less flexible. We recommend you start with the highest-level API first and get everything working.

If you need additional flexibility for some special modelling concerns, move one level lower. Each level is built using the APIs in lower levels, so dropping down the hierarchy should be reasonably straightforward.

**Tensor in Machine Learning?**

The question of what is a tensor in machine learning is a broad question and can be answered in many ways. We start by defining TensorFlow first, in the context of Machine Learning.

TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning, on the other hand, is a category of machine learning models that use multi-layer neural networks.

Machine Learning has helped us build complex applications with great precision. It can solve problems from a wide range of datasets, including images, videos, text or audio.

The primary reason for its popularity is the ease with which developers can build and deploy multiple applications. TensorFlow was created with processing power limitations in mind. The library can be run on computers of all kinds, even on smartphones.

You may also look for research papers on what is a Tensor in Machine Learning for more details.

**Tensor in Deep Learning?**

The question ‘What is a Tensor in Deep Learning ?’ can be explained in many ways. Here, we start by explaining the conventional setup and then move on to ‘What is a Tensor in Deep Learning?’, in the context of neural networks.

Traditional neural networks relied on shallow nets, composed of one input, one hidden layer, and one output layer.

Deep-learning networks, unlike traditional neural networks, are capable of discovering hidden structures within unlabeled and unstructured data (that may include images, sound, and text).

TensorFlow is by far, one of the best libraries to implement Deep Learning. It may be explained as a software library for numerical computation of mathematical expression, using data flow graphs.

The nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. TensorFlow was created by Google and tailored for Machine Learning in 2015 and is extensively used to develop solutions with Deep Learning.

You may also look for research papers on what is a Tensor in Deep Learning for more details.

**How is TensorFlow Better?**

TensorFlow applications can be run on almost anything; a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs. If you are using Google’s own cloud, you can run TensorFlow on Google’s custom TensorFlow Processing Unit (TPU) silicon for further acceleration.

The resulting models created by TensorFlow, though, can be deployed on most any device where they will be used to serve predictions.

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TensorFlow competes with a slew of other machine learning frameworks like PyTorch, CNTK, and MXNet are three major frameworks that address many of the same needs.

TensorFlow has a very wide spectrum of use cases compared to available solutions on the market. It targets ML projects of any size, in both research and industry, and deployment in the cloud, and the edge. PyTorch on the other side seems to have a much sharper focus, targeting production and smaller-sized projects.

### **Career Prospects in TensorFlow and Deep Learning**

Data that is derived from multiple sources today is unstructured. It may include images, sound, and textual data. Deep Learning is inextricably used with TensorFlow to address a wide range of problems. These may range from image classification and handwriting recognition to machine translation, leading to a large number of job opportunities in the days to come.

According to Deep Learning Platforms report, Deep Learning market is expected to grow at a CAGR of close to 42% in 2018 and beyond. Adoption of cloud-based technology and Big Data analytics will continue to drive the growth of the market.

You need to have a basic knowledge of complex Mathematics such as calculus, statistics, probability and linear algebra to be a Deep Learning expert. Mathematics is the base of DL since programming is just a way of teaching the computer the advanced concepts.

You also need to have a strong foundation n Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The requirement will be to work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.

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