What is TensorFlow? Machine Learning Library Explained
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What is TensorFlow? The Machine Learning Library Explained

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The deep learning artificial intelligence research team at Google, Google Brain, in the year 2015 developed a software library which they named as TensorFlow for Google’s internal use. This Open-Source Software library is used by the research team to perform several important tasks.

TensorFlow is at present the most popular software library. There are several real-world applications of deep learning that makes TensorFlow popular. Being an Open-Source library for deep learning and machine learning, TensorFlow finds a role to play in text-based applications, image recognition, voice search, and many more. DeepFace, Facebook’s image recognition system, uses TensorFlow for image recognition. It is used by Apple’s Siri for voice recognition. Every Google app that you use has made good use of TensorFlow to make your experience better.

What are the Applications of TensorFlow?

  • Google use Machine Learning in almost all of its products: Google has the most exhaustive database in the world. And they obviously would be more than happy if they could make the best use of this by exploiting it to the fullest. Also, if all the different kinds of teams — researchers, programmers, and data scientists — working on artificial intelligence could work using the same set of tools and thereby collaborating with each other, all their work could be made much simpler and more efficient. As technology developed and our needs widened, such a toolset became a necessity. Motivated by this necessity, Google created TensorFlow  — a solution that they have been long waiting for.
  • TensorFlow bundles together the study of Machine Learning and algorithms and will use it to enhance the efficiency of its products — by improving their search engine, by giving us recommendations, by translating to any of the 100+ languages, and more.

What is Machine Learning?

A computer can perform various functions and tasks relying on inference and patterns as opposed to the conventional methods like feeding explicit instructions, etc. The computer employs statistical models and algorithms to perform these functions. The study of such algorithms and models is termed as Machine Learning.

Deep learning is another term that one has to be familiar with. A subset of Machine Learning, deep learning is a class of algorithms that can extract higher-level features from the raw input. Or, in simple words, they are algorithms that teach a machine to learn from examples and previous experiences. 

Deep learning is based on the concept of Artificial Neural Networks, ANN. Developers use TensorFlow to create many multiple layered neural networks. Artificial Neural Networks, ANN, is an attempt to mimic the human nervous system to a good extent by using silicon and wires. The intention behind this system is to help develop a system that can interpret and solve real-world problems like a human brain would. 

What makes TensorFlow popular?

  • It is free and open-sourced: TensorFlow is an Open-Source Software released under the Apache License. An Open Source Software, OSS, is a kind of computer software where the source code is released under a license that enables anyone to access it. This means that the users can use this software library for any purpose — distribute, study and modify — without actually having to worry about paying royalties.
  • When compared to other such Machine Learning Software Libraries — Microsoft’s CNTK, or Theano — TensorFlow is relatively easy to use. Thus, even new developers with no significant understanding of machine learning can now access a powerful software library instead of building their models from scratch.
  • Another factor that adds to its popularity is the fact that it is based on graph computation. Graph computation allows the programmer to visualize his/her development with the neural networks. This can be achieved through the use of Tensor Board. This comes in handy while debugging the program. The Tensor Board is an important feature of TensorFlow as it helps monitor the activities of TensorFlow– both visually and graphically. Also, the programmer is given an option to save the graph for a later use.  

Tensors

All the computations associated with TensorFlow involves the use of tensors. This leads to an interesting question :

What are Tensors?

It is a vector/matrix of n-dimensions representing types of data. Values in a tensor hold identical data types with a known shape. This shape is the dimensionality of the matrix. A vector is a one-dimensional tensor; matrix a two-dimensional tensor. Obviously, a scalar is a zero dimensional tensor.

In the graph, computations are made possible through interconnections of tensors. The  mathematical operations are carried by the node of the tensor whereas the input-output relationships between nodes are explained by a tensor’s edge.

Thus TensorFlow takes an input in the form of an n-dimensional array/matrix (known as tensors) which flows through a system of several operations and comes out as output. Hence the name TensorFlow. A graph can be constructed to perform necessary operations at the output.

Applications

Below are listed a few of the use cases of TensorFlow:

 

  • Voice and speech recognition: The real challenge put before programmers was that a mere hearing of the words will not be enough. Since, words change meaning with context, a clear understanding of what the word represents with respect to the context is necessary. This is where deep learning plays a significant role. With the help of Artificial Neural Networks or ANNs, such an act has been made possible by performing word recognition, phoneme classification, etc.

Thus with the help of TensorFlow, artificial intelligence-enabled machines can now be trained to receive human voice as input, decipher and analyze it, and perform the necessary tasks. A number of applications makes use of this feature. They need this feature for voice search, automatic dictation, and more.

Let us take the case of Google’s search engine as an example. While you are using Google’s search engine, it applies machine learning using TensorFlow to predict the next word that you are about to type. Considering the fact that how accurate they often are, one can understand the level of sophistication and complexity involved in the process.

  • Image recognition: Apps that use the image recognition technology are probably the ones that popularized deep learning among the masses. The technology was developed with the intention to train and develop computers to see, identify, and analyze the world like how a human would.  Today, a number of applications finds these useful — the artificial intelligence enabled camera on your mobile phone, the social networking sites you visit, your telecom operators, to name a few.

In image recognition, Deep Learning trains the system to identify a certain image by exposing it to a number of images that are labelled manually. It is to be noted that the system learns to identify an image by learning from examples that are previously shown to it and not with the help of instructions saved in it on how to identify that particular image.

Take the case of Facebook’s image recognition system, DeepFace. It was trained in a similar way to identify human faces. When you tag someone in a photo that you have uploaded on Facebook, this technology is what that makes it possible. 

Another commendable development is in the field of Medical Science. Deep learning has made great progress in the field of healthcare — especially in the field of Ophthalmology and Digital Pathology. By developing a state of the art computer vision system, Google was able to develop computer-aided diagnostic screening that could detect certain medical conditions that would otherwise have required a diagnosis from an expert. Even with significant expertise in the area, considering the amount of tedious work one has to go through, chances are that the diagnosis vary from person to person. Also, in some cases, the condition might be too dormant to be detected by a medical practitioner. Such an occasion won’t arise here because the computer is designed to detect complex patterns that may not be visible to a human observer.    

TensorFlow is required for deep learning to efficiently use image recognition. The main advantage of using TensorFlow is that it helps to identify and categorize arbitrary objects within a larger image. This is also used for the purpose of identifying shapes for modelling purposes. 

  • Time series: The most common application of Time Series is in Recommendations. If you are someone using Facebook, YouTube, Netflix, or any other entertainment platform, then you may be familiar with this concept. For those who do not know, it is a list of videos or articles that the service provider believes suits you the best. TensorFlow Time Services algorithms are what they use to derive meaningful statistics from your history.

Another example is how PayPal uses the TensorFlow framework to detect fraud and offer secure transactions to its customers. PayPal has successfully been able to identify complex fraud patterns and have increased their fraud decline accuracy with the help of TensorFlow. The increased precision in identification has enabled the company to offer an enhanced experience to its customers. 

A Way Forward

With the help of TensorFlow, Machine Learning has already surpassed the heights that we once thought to be unattainable. There is hardly a domain in our life where a technology that is built with the help of this framework has no impact.

 From healthcare to entertainment industry, the applications of TensorFlow has widened the scope of artificial intelligence to every direction in order to enhance our experiences. Since TensorFlow is an Open-Source Software library, it is just a matter of time for new and innovative use cases to catch the headlines.

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