What is TensorFlow?

TensorFlow is an open source library, developed by Google, created for the specific purposes of numerical computing using data flow graphs as a source. Two of TensorFlow's most important applications are machine learning and deep neural network research.

Unlike many libraries, TensorFlow works on virtually every conceivable platform, from CPUs, GPUs, mobile and embedded devices, and tensor processing units (specialized pieces of hardware that use tensor math).

The background

TensorFlow was originally created as a deep learning project by the Google Brain team. Since its inception, TensorFlow has been deployed across the Google ecosystem, in tools such as:

• Google Assistant
• Google Photos
• Gmail
• Google search

And considering that Google has the largest machine learning infrastructure on the planet, it made sense for the company to allow other companies and developers to benefit from its platform. However, it was only after the creation of TensorFlow that Google was able to share this vast platform. With the TensorFlow tool library, any developer can add deep learning to their software.

How does TensorFlow work?

TensorFlow uses a specific set of modules (which includes APIs for Python, C, and C++) to allow you to build and run TensorFlow calculations. The data flow graphs resulting from these calculations are stateful, meaning that the program monitors the state of the interaction.

To be more specific, TensorFlow sorts through layers of data, called nodes, to discover increasingly complicated data about an image. As TensorFlow digs deeper into nodes, it can ask more complicated questions.

For example: in the first node, you might recognize a round shape. As TensorFlow digs deeper, it can recognize the shape of an eye. Deeper still and that eye becomes feline. This input process, which flows through data layers to the output, is called a tensor.

The current iteration of TensorFlow allows you to write code that creates a computational graph, which is a data structure that describes the calculation you want to perform. There are a number of advantages to this process. On the one hand, graphics can be executed immediately or saved and later executed on different platforms. Graphics can also be deployed to a production environment without needing to also deploy build code. The only thing required is an available runtime that supports the TensorFlow graph.

Another advantage is that the TensorFlow graph can be easily optimized for any platform. This makes it possible to train on a much larger platform and then transfer to a much less powerful platform (like a mobile device).

TensorFlow use cases

The application of TensorFlow is fascinating. With the help of tensors, an app like Google Photos is able to accurately recognize locations in images. For example, Photos uses it to locate a specific item in a photo (for example, a bridge or a statue) and know exactly where the photo was taken. The TensorFlow-enabled application can then act based on this new information.

This could all work like this: the app views the user's images, locates the Empire State Building and knows that the photo was taken in New York. The app can then display New York-specific ads to the user.

TensorFlow is not just limited to images. Another use case is voice and sound recognition. In fact, speech and sound recognition is one of the most used applications of TensorFlow. Google Assistant is the most obvious example, but there are other very important use cases that TensorFlow can be applied to, such as:

• Image recognition
• Object Tagging Videos
• Autonomous cars
• Sentiment Analysis
• Fault detection
• Text summary
• Mobile image and video processing
• Aerial, land and sea drones

TensorFlow components

TensorFlow has several pieces that come together to form the whole. Some of these pieces include:

TensorFlow.js

Allows the use of standard JavaScript models and can build and train models directly in JavaScript.

Federated TensorFlow

An open source framework for experimenting with machine learning, using decentralized data.

TF Privacy

A library for training privacy-centric machine learning models.

tf.function

Enables the transformation of a subset of Python syntax into portable, high-performance graphics.

TensorFlow Probability

A Python library for combining probabilistic models and deep learning.

Tensor2Tensor

A library of deep learning models and datasets.

It's all about machine learning

Machine learning is in everything. With the help of machine learning, devices continue to become smarter and more efficient. Considering that data is the lifeblood of business, every company has become dependent on the information it provides. From customer data to B2B information (and everything in between), companies have become deeply dependent on data.

With the help of TensorFlow and machine learning, your business will be better equipped to take advantage of the data available. That's because it can help you do the following:

• Predict customer behaviors and purchasing patterns to help you refine your customer interactions and better recommend products.
• Anticipate machine maintenance needs.
• Eliminate manual data entry.
• Detect spam.
• Analyze financial data.
• Use images for data, pattern recognition, and database knowledge discovery.
• Diagnose medical conditions.
• Improve cybersecurity.

Imagine how challenging it would be to tackle all these problems without the help of machine learning, especially when big data is applied to the equation. While you can effectively handle some of these tasks with smaller amounts of data, once you are dealing with thousands and hundreds of thousands of data points, it becomes impossible to understand that data manually.

And predict behavior? Unless your company has a data scientist available, this is not a good start. In other words, for any reliable level of predictive computing, you need machine learning. And because TensorFlow was released with an open source license, you can (with the right development team) integrate this incredible technology into your applications and systems.

A Holistic Approach to TensorFlow