What is TensorFlow | Introduction To TensorFlow

Introduction

The upward movement in the popularity of artificial intelligence and deep learning has instigated the growth of TensorFlow. It's an open-source AI library employed by companies to enable data flow graphs for building models. Knowing about the basics of TensorFlow has become crucial to pursue a career in Artificial Intelligence. This introduction or what is TensorFlow is a great step in this direction.

What is TensorFlow?

So, what is TensorFlow? TensorFlow can be best described as an open-source platform for ML through data flow graphs. It is one of those platforms today that is used heavily by data scientists, educators and software developers. It also supports conventional machine learning.

This machine learning framework gives support to data in the shape of tensors. These are multi-dimensional arrays of greater dimensions, managing gigantic volumes of data becomes easier with arrays with several dimensions. It employs the key concept or idea of graphs of data flow with edges and nodes.

Since the implementation method is in graphs and tables, expanding TensorFlow code across a group of GPU-equipped machines is pretty straightforward. It supports many programming languages, among which Python and JavaScript (JS) are the most popular ones. It also supports Swift, Go, C, Java and C#.

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History of TensorFlow

TensorFlow's first public appearance was in 2015 and its first stable version came out in 2017. Created and maintained by Google, it has risen to become one of the leading frameworks for DL and ML projects globally. It encompasses a gigantic library for large-scale ML as well as numerical computation. Here is a brief history of TensorFlow's biggest milestones-

• December 2017 saw the release of Kubeflow for the deployment and operation of TensorFlow on Kubernetes.
• March 2018 witnessed the release of TensorFlow 1.0 for ML in JavaScript.
• Jan 2019 was when TensorFlow 2.0 was released, adding a good number of components to this platform.
• TensorFlow Graphic was released in May 2019 for DL in computer graphics.

What is Tensorflow Used For?

After discussing 'what is TensorFlow', another common question here- what is TensorFlow used for. This section gives an answer to this question and throws light on the varying nature of this platform.

• Image processing & video detection- This machine learning framework is used for image processing and video detection. One great example is the airplane manufacturing giant Airbus that uses it for extracting and analyzing details from satellite images. This helps in delivering important real-time information to clients.
• Text recognition- Business can use this platform for classifying text and determining the true intent of clients upon receiving calls.
• Time series algorithms- It's another impeccable use of this platform. Kakao is one name that uses it for predicting the completion rate and speed of ride-hailing requests.
• Modeling- This machine learning framework is heavily used for generative modeling and deep transfer learning. It helps companies recognize complex and temporarily varying fraud patterns. Experience of legitimate customers is improved via expedited customer identification.
• Tweet prioritization- Twitter is known to have utilized this open-source framework for building its Ranked Timeline. This ensured that its users didn't miss any most important tweets even while following a plethora of users.

Related Article- Machine Learning Interview Questions

Why is TensorFlow popular?

Another important question for now is- why is TensorFlow popular? Its popularity is a result of its unique and global uses. It has become a chosen platform among businesses of all kinds and sizes today.

• TensorFlow made ML easy- It offers pre-trained models, high-level APIs and data to support easy building of Machine Learning models.

• ML becomes a service- ML becomes a service when TensorFlow is employed. The required model from the TensorFlow models can be used.

• Ready-made models for production purposes- It supports various pre-trained models, instantly usable for experiment and production.

• Used by researchers- Many researchers and even students employ TensorFlow for their research as well as model building.

• Used by many companies- It's employed by various companies such as Google, DeepMind, Intel, Uber, Twitter, DropBox and AirBnb.

Components of TensorFlow

There are plenty of components of TensorFlow that aid in the creation and execution of programs.

Tensors

The term TensorFlow comes from its core structure- tTensor. All computations in this machine learning framework need tensors for executing a program. A tensor refers to an n-dimensional matrix or vector that may comprise all data types. Every tensor value carries the same data type with a known (or partially known) form. The shape of the input data defines the dimensionality of the matrix. It can be derived from either the input data or even the outcome of a process. All methods or functions are carried out in a graph that is defined by utilizing the TensorFlow library. A graph refers to a sequence of functions carried out consecutively. Every single operation that's represented in a graph is called an op node.

Graphs

A graph refers to an important component of this platform. It helps the graphical representation of all the programmed processes. The graph framework is utilized for representing complex AI or ML processes. These also aid the user in collecting and describing the sequence of computations that the model intends to perform. Top advantages of using graphs are-

• These graphs have a portability feature. It enables the user to save it for undertaking computational tasks in the future.
• Graphs can be run on GPUs, CPUs and mobile operating systems.
• Visualizing the operations that are being performed and how to get the output becomes easy with the aid of edges and nodes represented by graphs.

When developing complex DL models, they comprise plenty of complicated processes with the input data that's stored in tensors. The flow of execution is defined to correctly perform the computations while using the data in tensors. A dataflow graph is used that aids in visualizing the flow of data. Dataflow graphs comprise of edges and nodes.

Final Thought For What is TensorFlow

This machine learning framework can simply be defined as an open-source framework and platform for machine learning. It encompasses various tools and libraries as per Python and Java. It is crafted with the purpose of training machine learning as well as deep learning models on the data. This blog has explained 'what is TensorFlow', along with its uses, components and reasons for popularity.

FAQs

Q1. What is the difference between tensor and TensorFlow?

This machine learning platform refers to a framework for defining and running computations including tensors. Tensor, on the contrary, is a generalization of matrices and vectors to exceptionally higher dimensions.

Q2. How to define this machine learning platform?

It can simply be defined as a 35–degree platform for ML.

Q3. Is TensorFlow a framework or library?

TensorFlow can be labeled as both a framework and a library.

Q4. Why is TensorFlow needed?

It's needed to implement best practices around model tracking, model retraining, data automation and performance monitoring.

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