Google TensorFlow is a machine learning platform designed to create AI programmes in the most developer friendly way possible. As one of the longer standing machine learning platforms, and with the ongoing support from Google and the external community, TensorFlow has become one of, if not the, most popular platforms for Artificial Intelligence.

What is Google TensorFlow?

Google TensorFlow has been developed by the Google brain team to allow developers to create AI programmes. It is often considered to be the most straightforward machine learning softwares which goes some way to explain the vast popularity of Google TensorFlow.

It is a platform for programming with linear algebra and statistics to build deep neural networks with high level code. It is accompanied by an open source library which allows developers to choose from an array of prewritten code and programmes to develop their application. Developers often question whether Google TensorFlow is a framework or a library, however, in reality, it acts as an all encompassing platform to support AI and machine learning programmes.

How does Google TensorFlow work?

TensorFlow offers developers a simplified neural network that is built up of tensors, the standard way of representing data in deep learning. It consists of objects and applications which are written in Python, while the mathematical operations are performed in C++ binaries.
Every TensorFlow neural network consists of at least 5 steps. These steps are:

  • Collection of data, this is where the majority of work is done;

  • Building of the neural network model, this requires just a few lines of code;

  • Training the network, this is done by a single line of code;

  • Evaluation, this is also done by a single line of code;

  • Prediction, once again just using a single line of code.

These five steps are used in all neural networks in TensorFlow and provide the most straightforward platform for developers to build their applications.

One popular use of TensorFlow is spam detection. This is used by countless organizations including Gmail and works by analyzing enormous datasets of messages which have been labeled as either “spam” or “not spam”, referred to as a “supervised learning algorithm”. The messages are converted into vectors, a mathematical representation of the data that can be analyzed by machine learning algorithms. Ongoing analysis of these messages allows the AI to be trained to locate key patterns in future messages which indicate the likelihood that the message is spam. TensorFlow can also use an “unsupervised learning algorithm” to analyze data which does not require existing, labeled data sets, however this is often less accurate than the supervised learning algorithm.

TensorFlow also comes with a collection of APIs, libraries and community resources that allows a variety of organizations to develop customer AI and machine learning platforms that are perfectly tailored to their requirements. TensorFlow is displayed exceptionally in many industries from; healthcare, where image recognition and classification is used in MRI brain scans to detect anomalies and offer potentially life saving insight; to fraud detection in financial organizations such as paypal, where the patterns of fraudsters are analyzed to protect the data of legitimate users.

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Why should organizations use Google TensorFlow?

TensorFlow is relatively long standing in the world of deep learning, allowing a vast ecosystem and community to develop around the platform. Regardless of the needs of an organization or development team, there is a version of TensorFlow that can be applied to their requirements.

Despite the complexity of deep learning, TensorFlow makes every effort to be as user friendly as possible. This is done by refining and simplifying code as much as possible and through the implementation of Keras, a user-friendly library which supports multiple backends and provides a Python interface when developing neural networks.

Advantages

Open source

 One clear advantage of Google TensorFlow is the open source nature of the platform, which has encouraged community development and allows developers to select from a vast range of prewritten code to apply to their application.

Debugging

 Built into the TensorBoard, debugging has been made as simple for developers as possible.

Free

Google TensorFlow is completely free.

Documentation

All developers are able to access extensive documentation for Google TensorFlow for free. Additionally, dedicated developers are able to achieve TensorFlow certification provided by Google, however this qualification comes at a cost.

Visualization

Arguably superior to competitors, TensorFlow offers comprehensive visualization elements to all developers.

TPUs

Tensor Processing Units (TPUs) are easily deployed on the cloud and function significantly faster than the CPU or GPU, although TensorFlow is not limited to TPUs and is compatible with all processing units.

Backed by Google

An undeniably advantage to TensorFlow is the backing of Google. Developers can be confident that TensorFlow will not be left behind and will receive regular updates and improvements.

Potential drawbacks of TensorFlow

  • Limited features on Windows – Windows users may find Google TensorFlow to be lacking in certain areas, as many features are only available on Linux.

  • GPU support – TensorFlow only has NVIDIA GPU support and Python Language support for GPU programming.

  • Excessive updates – With updates rolled out at incredible frequency, developers will need to uninstall and reinstall multiple times just to stay up to date.

  • Homonyms – The use of homonyms in TensorFlow means certain words are used for one or more components, often leading to confusion.

Who uses Google TensorFlow?

According to Enlyft, Google TensorFlow controls over 78% of the market share for Artificial Intelligence. With over 169 thousand stars on GitHub, it may be easier to list machine learning programmes that do not use TensorFlow.

TensorFlow can be employed throughout countless industries, including: healthcare, with developments becoming vital in MRIs and other imaging scans; self-driving cars, ensuring travelers are safe; translation services; social media; financial organizations, to increase fraud protection, and almost all Google applications.

Frequently
Asked Questions.

Whether you’re a developer, researcher, or just curious about this powerful machine learning platform, you’ll find essential information here to better understand its features, accessibility, and usage.

Is TensorFlow Free?

Google TensorFlow is an open source machine learning platform. It is free for all developers and always will be. Additionally, developers have access to a vast host of community features. The only paid element of TensorFlow is the certifications available from Google, at the cost of $100.00

In TensorFlow, objects and applications are written in the Python coding language while the mathematical operations are performed in C++ binaries. Knowledge of both of these languages will be invaluable for developers looking to learn TensorFlow.

There does not seem to be any consensus on whether TensorFlow is easy to learn. Difficulty comes down to the requirements of the developers. While it is commonly thought that there are easier platforms, the certification provided by TensorFlow can help experienced developers build a comprehensive knowledge. Despite this, novice developers, researchers and academics seem to agree that TensorFlow has a very steep learning curve and often struggle with the lack of flexibility.

As of 2022, Google TensorFlow controls nearly 80% of the market share for artificial intelligence platforms. There are a multitude of reasons for this, however some of these reasons include the fact that it was developed by the Google Brain Team and is continually maintained and updated by Google and that it is both free and open source, meaning developers from any organization can use it without cost.

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