Today, technology is the focal point in our everyday lives. The need for intelligent apps and systems is essential. Using Machine Learning and deep learning methods, we can now build intelligent mobile apps. Google developed this framework “TensorFlow” to ease the process of converting data into actionable insights.    

What is TensorFlow?

TensorFlow is an open-source application-based library based on deep learning neural networks. It was conceptualised by Google Brain Team, DistBelief. After some modifications, it was released as TensorFlow in 2015. TensorFlow uses a combination of machine learning with deep learning. 

The name “TensorFlow” is derived from the operations in which neural networks perform on multidimensional data arrays or tensors! It’s literally a flow of tensors. 

TensorFlow use cases are implemented  by training and running deep neural networks for:

• handwritten digit classification
• image classification using TensorFlow
• word embeddings
• recurrent neural networks
• sequence-to-sequence models for machine translation
• natural language processing
• PDE (partial differential equation) based simulations. 

Most importantly, TensorFlow supports production prediction at scale.

How TensorFlow works

TensorFlow is basically a bunch of machine learning and deep learning models and algorithms and that are used by way of a common metaphor. Python is used as a front-end API for building mobile and web applications in the framework while C++ is used for execution.

Using TensorFlow, developers can create dataflow graphs—structures that describe how data flows through a graph, or the various processing nodes. Tensor is a matrix of n-dimension that represents all types of data and nodes are the mathematical operations. 

TensorFlow applications can be run on almost any target: a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs.

TensorFlow Use Cases in Mobile Apps

TensorFlow is basically an AI library that utilizes data flow graphs for building models. You can use it for classification, perception, understanding, discovering, predicting and creating. Some use cases that Systango has had the chance to work on for its clients are:

1. TensorFlow Voice Recognition

Tensorflow voice recognition and sound recognition are the most common TensorFlow use cases. Using good data feed, we can capture the following audio signals:

• TensorFlow Voice Recognition: used in IoT, automotive, security, and UX/UI
• Voice search: used by telecom mobile manufacturers
• Sentiment analysis: used in CRM
• Flaw detection (engine noise): used in the automotive and aviation industry

In our everyday life, we use Apple’s Siri, Google Now for Android, and Microsoft Cortana for Windows Phones. TensorFlow voice recognition and sound recognition can also be used to Identify languages and speech-to-text conversions.

2. Text-based applications

Among Text-based applications, TensorFlow use cases include Sentiment analysis and threat detection on Social Media. Fraud detection in the Insurance and Finance Industry. These TensorFlow use cases have a huge impact on decision making and business planning.

• Google Translate:  works on any app and supports 100+ language translations
• SmartReply: Automatic e-mail responses we use on a regular basis.

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3. Image Classification Using TensorFlow

Image classification using TensorFlow is one of the most important TensorFlow use cases using which we can identify not just objects but also people. This helps us gain better insights about content and context.

TensorFlow is used across:

• social media channels: Tagging photos ( Facebook’s Deep Face). 
• Engineering apps: identifying shapes for modeling purposes ( 3D space construction from 2D images)
• Google Photos: Here, Image classification using TensorFlow helps you organise your photos based on people.

4. Time series

Let’s look at how companies and you benefit using this TensorFlow use cases.

Recommendation: Companies like Amazon, Google, Facebook, and Netflix, are able to analyse customer interactions using this algorithm. They then compare these interactions with other users to understand a customer’s buying mindset.

These recommendations keep changing over time. 

For example: offering discounts and offers, making recommendations about the next movie, etc.

There are quite a few similar uses in accounting, finance, security and IoT, government, etc.

5. Video detection

Motion detection, security, airports, real-time threat detection in gaming, and in UX/UI fields are also TensorFlow use cases. Video detection along with Image Classification using TensorFlow has a lot of applications.

Machine Learning can be leveraged to build amazing apps in the healthcare industry by detecting disease patterns, predicting personalised treatment for individuals, resource allocation and management. TensorFlow use cases are present in almost every industry be it Social Networks, Finance, Food or E-Commerce.

TensorFlow is a tool that allows us to unleash the power of Machine Learning and Artificial Intelligence. Using TensorFlow is the way to make your app a super app by adding multiple services to your platform and getting ahead of your competition. If you want to understand how you can implement TensorFlow use cases and what effects it can have on your application, Systango is happy to help!

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