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What is TensorFlow and how does it work?
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What is TensorFlow and how does it work?
  • Introduction
  • What is TensorFlow and how does it work?
  • What is it? How does it work?
  • What is TensorFlow?
  • How does TensorFlow Work?
  • TensorFlow Benefits
  • TensorFlow versus Competition
  • Takeaway

Introduction

If you are interested in machine learning, chances are that you have heard the name TensorFlow.

What is it? How does it work?

These are a few questions that might have crossed your mind.

Computer programs can be viewed as a sequence of operations that transform input data to output data. Computation is the process of transforming input data to output data. The computational model that TensorFlow follows is known as “TensorFlow”.

This blog will offer insights into what TensorFlow is and how it works.

So, let’s get started!

What is TensorFlow?

TensorFlow is an open-source software program that can be used for a variety of functions in Machine Learning and Deep Learning.

When the input data changes, it computes new values of output data automatically in an automated fashion.

TensorFlow uses data flow graphs and it can be used to build neural networks, which is the most popular application of TensorFlow.

TensorFlow was developed by the Google Brain team for internal use.

Later, it became an open-source project to support a broader range of users.

Google TensorFlow uses GPUs (Graphics Processing Units) and CPUs(Central Processing Units), which accelerates computation and is highly scalable. In addition, TensorFlow supports distributed computation on many devices.

So, now you know what TensorFlow is, time to see how it works!

How does TensorFlow Work?

TensorFlow allows developers to create dataflow graphs that can be used for processing large datasets much faster than other models of computation. Each node in a graph represents a mathematical operation, and every connection or edge between nodes is a tensor or multidimensional data array.

< TensorFlow Graph Image >

TensorFlow allows programmers to express these high-level abstractions in the Python language. TensorFlow is a Python-based application. Nodes and tensors are both Python objects.

The math operations are not performed in Python. They are delegated to libraries written in C++ and run using platform-specific optimisations. Python performs tasks between pieces of traffic and provides programming abstractions to hook it all together.

TensorFlow applications can be run on various devices, including local machines, in the cloud and mobile phones. With Google’s cloud, there is the option to use TensorFlow on their custom-produced TPU silicon for accelerated performance. The models created by TensorFlow are available on nearly any device that can run them and offer predictions.

TensorFlow 2.0, which was released in October 2019, revamped the framework based on user feedback to enhance its performance and make it easier to work with (by using the relatively simple Keras API for model training).

New support and API for TensorFlow Lite make it easier to run distributed training. Some necessary changes must be made to older programs written for previous versions of TensorFlow to take advantage of updated features in TensorFlow 2.0

Let’s say you were diligent about the dog’s vaccinations all year long, but one day it was just too hot and humid, and you couldn’t get out of the house. All it takes is one sick dog to cross paths with a healthy, infected cat, and your handsome little dog becomes a carrier of rabies. Suddenly, you find yourself making that emergency trip to the veterinarian’s office so they can give him his rabies shot while he still has symptoms (to minimise his risk of passing on the disease).

And that’s what tensors are for – they help you make sense of an ever-growing number of variables so you can figure out where and how to focus your time, money, and resources. TensorFlow is a programming library designed by Google for processing big data in a distributed environment. It can run on multiple CPUs and distributed devices, like GPUs.

TensorFlow Benefits

Abstraction is TensorFlow’s most compelling feature when you want to build a machine learning model. Abstractions hide the details and help you to understand how your model works.

Instead of implementing algorithms or syncing functions, developers can focus on designing the ins and outs of the application. TensorFlow simplifies how these things work behind the scenes.

TensorFlow provides conveniences for developers, such as debugging and introspection into Tensorflow apps. The eager execution mode lets you evaluate each graphical operation individually and transparently instead of assessing the entire graph. TensorBoard is a web-based visualisation suite that enables users to inspect and profile the way their graphs run.

Google’s tremendous expertise in artificial intelligence and machine learning help make TensorFlow robust as a deep learning framework. Google has driven the rapid pace of development behind TensorFlow while also offering many significant offerings for developers using its framework.

Among other things, this includes an accelerated performance with TPU silicon in Google’s cloud; a hub for sharing models created with the framework, versions emphasising JavaScript and Python accessibility on HTML pages or mobile devices.

One caveat- Some minor details of TensorFlow’s implementation can occasionally cause training models issues. The model-training results will not be deterministic in such cases. Sometimes, models built on one system will differ from those created using different techniques.

The reasons for this are slippery. For instance, the random numbers may be used as a seed and have unexpected results if not sown in an orderly fashion. Certain non-deterministic behaviours occur when using GPUs. TensorFlow is considering more controls that will affect determinism in a workflow.

TensorFlow versus Competition

TensorFlow competes with several other machine learning frameworks. CNTK, PyTorch, and MXNet are three major frameworks that address many of the same needs. TensorFlow is the world’s most popular tool for training deep models, often used to replicate human cognition.

Apache MXNet is scalable due to its capability of using multiple GPUs and computing machines. It also supports language APIs- Python, Scala, C++, JavaScript, R, Julia, and Perl– though its native API is less costly to work with TensorFlow.

CNTK, Microsoft’s new Graph Toolkit for deep learning, is similar to TensorFlow in the sense that both use a graph structure to describe data flow. CNTK is an open-source framework that handles many neural network jobs faster and has a wider range of languages for building networks. TensorFlow is currently much easier to learn.

PyTorch has many features in common with TensorFlow, and it’s built with Python. PyTorch and TensorFlow both have their strengths. PyTorch is best when you need to get things up and running quickly, but TensorFlow will serve you better for larger projects with more complex workflows.

Takeaway

TensorFlow is applicable in any applications that require numerical computation. It’s used in all kinds of fields and industrial application areas, such as:

Artificial Intelligence (AI) / Deep Learning covers neural network-based machine learning, speech recognition, and language translation to automatic image caption generation.

Computer Vision (CV) involves machine learning for pattern and objects recognition in images, videos, and other content.

Natural Language Processing (NLP) covers the statistical analysis of large volumes of unstructured text data, such as sentiment analysis or spam detection.

Other Machine Learning domains involve any software application that uses analytical models to predict future events based on experience.

Data Analysis – Statistical Modeling, Financial Modeling, and Predictive Analytics

TensorFlow includes linear algebra and calculus libraries that researchers rely on for data analysis.

Conclusion

So what are your insights about TensorFlow and its application benefits?

Please share your thoughts with us.

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