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Introduction

Machine Learning (ML) gives systems the ability to learn from previous experiences/interactions and improve upon them, without being programmed. This enables companies to deliver personalized and seamless customer experience, besides being able to make informed decisions quickly. While understanding and developing complex ML frameworks can be difficult, TensorFlow – an open source Machine Learning library based on Deep Learning neural networks – is making this task easier. Initially developed by the Google Brain Team for use internally, it was released in open source in 2015. According to its official website, TensorFlow “offers application programming interfaces (APIs) for beginners and experts to develop for desktop, mobile, web, and cloud”. This is a software library for numerical computation that deploys data flow graphs and is being increasingly used by organizations to develop smart ML and deep learning models. TensorFlow can be made to run on multiple CPUs, GPUs as well as mobile operating systems such as Android and iOS. TensorFlow works on data flow graphs to represent computation. Each graph contains nodes or operations, which are units of computation. Therefore, a tensor is how the data is represented in TensorFlow, which is a multi-dimensional array of data/numbers.

Applications of TensorFlow

Every major company or product today uses ML and Deep Learning in some way to make assessments and recommendations. TensorFlow, which is a combination of both ML and Deep Learning technologies, allows businesses/researchers/developers/data scientists to take advantage of huge data sets and make predictions/suggestions. The software library is written in Python (it also uses other languages such as C++) – which is an easy programming language to understand and learn – to offer a smooth front-end API for developing applications. Hence, it allows developers, irrespective of whether they are beginners or experts, to easily and efficiently use this framework to test ideas and build machine learning solutions, instead of dealing with complex algorithms.

It can be applied for speech/voice/image recognition, video processing, pattern recognition, natural language processing, detecting sentiments in text/speech, error detection, and predictive analytics, among others. Google uses TensorFlow across its many products/areas, which includes its speech recognition systems, Google Photos, in Gmail and search, etc. For example, Google’s RankBrain, which is used to provide users with more pertinent or appropriate search results, utilizes the Tensor Processing Unit (TPU) to process queries/requests. TensorFlow can be applied across sectors, especially those juggling with big data and its challenges, which includes retail, aviation, defense/security, automation, telecommunications, social media, finance, and healthcare, to name a few. According to TensorFlow’s official website, companies that are currently using this open source project include Uber, Airbnb, Intel, CocaCola, SAP, Qualcomm, Dropbox, Bloomberg, Lenovo, LinkedIn, Airbus Defence & Space, eBay, and Kakao, among others.

Advantages of TensorFlow

  • It is open source, scalable, enabling quick updates and knowledge sharing.
  • It can be used to develop largescale and complicated Machine Learning and Deep Learning models.
  • It is easy to use. TensorFlow makes it easier for developers, who are new to Machine Learning/Deep Learning to get started. Experts, on the other hand, can make use of its advanced capabilities.
  • It has a flexible architecture, which facilitates numerical and graphical computation across a range of platforms and can be used across multiple domains. Hence, it does not require additional hardware support.
  • It can be used for research and development as well as production. Further, since developers do not have to understand complicated algorithms and owing to its multiple-platform usability advantage, it speeds up the process of development. This enables companies to run experiments quickly, besides being able to capture new trends, resolve issues and get actionable insights faster.
 

 

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