Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconTensorFlow Explained: Components, Functions, Supported Platforms & Advantages

TensorFlow Explained: Components, Functions, Supported Platforms & Advantages

Last updated:
28th May, 2020
Views
Read Time
7 Mins
share image icon
In this article
Chevron in toc
View All
TensorFlow Explained: Components, Functions, Supported Platforms & Advantages

What is TensorFlow, exactly?

If you’re interested in machine learning and deep learning, then you’ve come to the right place! In this article, we’ll explore what TensorFlow is, how it works, and what it’s made of. Read on to learn it all!

Top Machine Learning and AI Courses Online

TensorFlow Explained

TensorFlow is a machine learning framework and a product of Google. It simplifies the tasks of model training, data acquisition, result refinement, and serving of predictions. It is an open-source deep-learning library, and Google uses it to empower their numerous technologies. TensorFlow makes many neural networking models and machine learning algorithms useful for computations and applications. 

For its front-end API, it uses Python, and you can use it to build applications. To execute the same apps, you can use the popular C++ language. TensorFlow is capable of training and running deep learning technologies to develop word embedding, digit classification, RNNs (recurrent neural networks), and many others.

Ads of upGrad blog

Google uses TensorFlow to enhance user experience and enhance its search feature. A great example of this is the autofill in the search bar of Google. Google has a vast dataset, and by using machine learning, they can enhance their users’ experience. 

Trending Machine Learning Skills

Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

TensorFlow Components

TensorFlow contains two primary components:

  • Tensor
  • Graphs

Tensor

Tensor is the main framework of Tensorflow. A tensor is a matrix or vector that has n-dimensions and represents all kinds of data. All the values present in a tensor possess identical data types with a shape, which is the dimensionality of the array or matrix. 

A tensor could originate from input or as a result of some computation. And all the operations in TensorFlow take place in a graph. The name of these operations is op node. A tensor is made up of an edge and a node. While the node has the mathematical process, the edge explains the relationship between the nodes. 

Graphs

TensorFlow is based on the graph framework. Its graph collects and explains the computations the system performs during the training session. The graph is quite portable and enables the preservation of calculations so you can use them whenever you require them. You can save the chart for future use as well. 

The computations in a graph take place through connecting the tensors. Graphs can run on different GPUs and CPUs. You can run them on mobile systems as well. 

Why is TensorFlow so Popular?

The creators of TensorFlow had created it to scale. Its accessibility is phenomenal because anyone can access it. Its library has various API that you can use to construct complex architectures such as Recurrent Neural Networks

It uses graphs to visualize the development of neural networks for the developer. It does so through Tensorboard and helps the developer in debugging the program, deploying at scale, and building robust solutions. It is the most popular deep learning framework library on GitHub. So you’ve understood by now as to why it’s so popular. 

How does TensorFlow Work?

You can build dataflow graphs by using TensorFlow. These graphs show how data goes through a series of nodes present in a graph. As we’ve mentioned earlier, these nodes represent mathematical operations, and the connections between these nodes are tensors. 

You can use the Python language to use all of these facilities. Python is quite easy to learn and gives you simple methods to convey complex abstractions. The nodes and tensors of TensorFlow are Python objects, and its applications are Python applications as well. 

But while you can use Python to work with nodes and tensors, you can’t use the same to perform mathematical operations. For that purpose, you’ll have to use C++. Python only handles the traffic between TensorFlow’s pieces. The transformations’ libraries in TensorFlow are C++ binaries, that’s why you’ll need to use C++ to work with them. 

Running TensorFlow applications is easy and convenient. You can run them on the cloud, a local machine, or even on a smartphone. You can use Google’s TensorFlow Processing Unit, too, if you’re using it on the cloud. Recently, TensorFlow 2.0 entered the market, and it has simplified the user experience further by incorporating the latest solutions (such as the Keras API). 

Supported Platforms

Let’s now focus on which platforms you would use to perform different operations with TensorFlow. We can classify its requirements into two sections: 

Development

This is the section when you train a model. For this phase, you should use a laptop or desktop computer. 

Inference

In this phase, you run TensorFlow on a platform. You can choose from various platforms such as mobile devices (Android or iOS), desktop PCs (Windows, Linux, or macOS), or on the cloud. 

After training a model, you have the option to use it on a different machine. TensorFlow allows training on GPUs as well. Stanford’s researchers discovered in 2010 that GPUs are excellent for algebra and matrix operations. Since that discovery, GPU has become a tool for those functions as well. And TensorFlow is compatible with GPUs. 

Read more: The What’s What of Keras and TensorFlow

Advantages of TensorFlow

Without mentioning the advantages of TensorFlow, we can’t answer the question, “What is TensorFlow?” properly.

Abstraction

Abstraction is the biggest advantage of TensorFlow. It allows you to solve your problem by focusing on the logic of the application. It removes the requirement of focusing on the minute details of an algorithm, or the method of producing an output from a particular input.

Convenient

TensorFlow has a very easy to use interface, which makes it a convenient tool for any developer. Particularly, it allows developers to debug their TensorFlow apps much easier. It has the eager execution mode through which you can check and edit every graph function transparently and separately. Otherwise, you would’ve needed to modify the entire graph as a single unit to modify or evaluate it properly. 

Another excellent example of TensorFlow’s convenience is TensorBoard. It’s the visualization tool of TensorFlow that lets you inspect graphs through a web-based and interactive dashboard. 

Google’s Backing

TensorFlow is a product of Google’s Brain Team, and that’s a major advantage in itself. Not only did that help TensorFlow in developing faster, but it also made it easier to deploy and use. Google’s TensorFlow Processing Unit silicon boosts its performance substantially and has provided it many capabilities. 

One Drawback

TensorFlow surely has multiple features, but it also has a drawback. Its implementation makes the job of getting deterministic training results rather difficult. In some cases, a model you trained on one system would behave differently on another system even if you feed them the same data. 

TensorFlow vs. Others

TensorFlow isn’t alone in the industry of machine learning frameworks. You also have PyTorch, Apache MXNet, and CNTK. 

Pytorch is based on Python and is quite similar to TensorFlow. The former is excellent for small projects, whereas the latter is better suited for long projects. CNTK is the Microsoft Cognitive Toolkit and is ahead of TensorFlow in many areas (faster handling of neural networks, graph structures, etc.). Still, it is much more difficult to learn in comparison to TensorFlow. 

Popular AI and ML Blogs & Free Courses

MXNet supports multiple language APIs, including Scala, Python, Perl, JavaScript, Go, and R. It is Amazon’s primary deep learning framework for AWS. The native APIs of MXNet is quite unpleasant to use when you compare them with the APIs of TensorFlow. 

Ads of upGrad blog

Also read: Tensorflow vs Pytorch – Comparison, Features & Applications

Final Thoughts

So, what is TensorFlow? We hope you found the answer in this article. If you’re interested in learning more about TensorFlow and its capabilities, then you can check out our blog. Here are some interesting TensorFlow project ideas to get you started. 

You can also take a look at our machine learning courses and start your learning journey there. Our comprehensive courses will help you get rid of all your doubts and develop the skills necessary to become a machine learning expert. IIIT-B & upGrad’s PG Diploma in Machine Learning & AI is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

Profile

Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Explore Free Courses

Suggested Blogs

Top 5 Natural Language Processing (NLP) Projects & Topics For Beginners [2024]
109272
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

30 May 2024

Top 8 Exciting AWS Projects & Ideas For Beginners [2024]
99088
AWS Projects & Topics Looking for AWS project ideas? Then you’ve come to the right place because, in this article, we’ve shared multiple AWS proj
Read More

by Pavan Vadapalli

30 May 2024

Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting
91382
Owing to the proliferation of Machine learning applications and an increase in computing power, data scientists have inherently implemented algorithms
Read More

by Pavan Vadapalli

25 May 2024

45+ Best Machine Learning Project Ideas For Beginners [2024]
331176
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

21 May 2024

Top 9 Python Libraries for Machine Learning in 2024
76220
Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learn
Read More

by upGrad

19 May 2024

Top 15 IoT Interview Questions & Answers 2024 – For Beginners & Experienced
65165
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

19 May 2024

40 Best IoT Project Ideas & Topics For Beginners 2024 [Latest]
769381
In this article, you will learn the 40Exciting IoT Project Ideas & Topics. Take a glimpse at the project ideas listed below. Best Simple IoT Proje
Read More

by Kechit Goyal

19 May 2024

Top 22 Artificial Intelligence Project Ideas & Topics for Beginners [2024]
421788
In this article, you will learn the 22 AI project ideas & Topics. Take a glimpse below. Best AI Project Ideas & Topics Predict Housing Price
Read More

by Pavan Vadapalli

18 May 2024

Image Segmentation Techniques [Step By Step Implementation]
64536
What do you see first when you look at your selfie? Your face, right? You can spot your face because your brain is capable of identifying your face an
Read More

by Pavan Vadapalli

16 May 2024

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon