What Is TensorFlow? Definition, Use Cases, and More

This open-source machine learning platform supports you in efficiently building machine learning models. TensorFlow assists with all stages of the process, from data preparation all the way through to running the models. TensorFlow gives you access to several tools and libraries for both machine learning and deep learning in several languages. Python is typically the go-to option for using TensorFlow, but it also supports Java, C++, JavaScript, Go, Swift, and C.

What is TensorFlow used for? 

Google developed TensorFlow to make building machine learning applications accessible across various industries. It’s a helpful tool for working with traditional machine learning. You can also use it to build deep learning models and train complex, data-intensive neural networks.

Some specific industries using TensorFlow include information technology, cybersecurity, e-commerce, social media, and health care. Businesses build applications using TensorFlow to perform tasks such as video detection, text recognition, transfer learning, image processing, and email response automation, with TensorFlow algorithms providing the ability to create a variety of different models.

Who uses TensorFlow?

Several professionals, including data scientists, statisticians, software developers, and machine learning developers, implement TensorFlow to develop applications.

Data scientists use TensorFlow to construct dataflow graphs that illustrate the way data flows through nodes. Nodes are computational units found in machine learning and deep learning algorithms that assign weights to data as it passes through the algorithm before ultimately converting to an output.

Statisticians benefit from the TensorFlow Probability library and its statistical analysis capabilities. It also allows you to combine deep learning and probability models, resulting in deep probabilistic models. This helps to make accurate predictions and develop a more robust understanding based on the data.

Software developers use TensorFlow to implement machine learning models for mobile applications, desktop and web applications, and the cloud. You can build programs compatible with iOS and Android for mobile applications. The TensorFlow.js library allows you to build web-friendly machine learning models accessible from your browser, convert existing Python-based models to run under Node.js, and retrain models using fresh data.

Machine learning developers use TensorFlow to create machine learning applications in areas such as natural language processing, computer vision, object detection, text recognition, and building convolutional neural networks. 

How TensorFlow works: Real-world use cases

Thousands of companies use TensorFlow to implement machine learning solutions. A few specific examples include the following:

• Coca-Cola used TensorFlow to develop its product code recognition platform, enabling mobile proof of purchase simply by scanning codes. This allowed Coca-Cola to save millions of dollars in production expenses.

• GE Healthcare uses TensorFlow to develop deep learning frameworks that enable its MRI scans to better look at brain scan features and produce more reliable results.

• Airbnb can implement machine learning models using TensorFlow to detect objects within images and classify images as well, ultimately leading to more positive experiences for guests.

• PayPal can identify unusual patterns to ensure users are legitimate and prevent fraudulent use with the help of generative modeling and deep learning in TensorFlow.

Is TensorFlow just Python, and what is the difference between PyTorch and TensorFlow?

Python is a language; TensorFlow is a framework. Although Python is integral to how TensorFlow works because it is one of the primary languages TensorFlow uses for its front-end application programming interface, the framework also relies on other languages, including Java and C++. PyTorch is another framework option, one even more integrated with Python. While TensorFlow tends to excel with large, complex, and AI-related projects, you might choose PyTorch for research-oriented development projects and scenarios in which you need an easier learning curve (beginners) or more flexibility to change things as you go (i.e., experimental projects). 

TensorFlow vs. TensorFlow Lite (LiteRT)

Another version of TensorFlow is TensorFlow Lite, now called Lite Runtime (LiteRT). TensorFlow Lite makes deep learning more accessible on mobile and embedded devices so that you can convert your already-trained models to a suitable format using TensorFlow Lite Converter. It lets you address the differences in file size and storage capacity so that when running applications on devices such as your mobile phone, the model doesn’t take up excessive space while still running at high speeds. Although your model's performance declines slightly as you convert it, the difference is minimal and still worthwhile for the improved speed you will experience on mobile devices.

You can use TensorFlow Lite for the same types of machine learning models you would build in TensorFlow, including computer vision, image processing, text recognition, and natural language processing.

What is TensorFlow in machine learning? Pros and cons of TensorFlow

Utilizing TensorFlow for your machine learning needs undoubtedly comes with many benefits. However, some challenges do exist as well.

Pros

• TensorFlow simplifies the process of developing machine learning models, assisting with training, reducing debugging, processing data, and deploying models.

• TensorFlow has cross-platform compatibility so that you can import models, and it is accessible in various programming languages.

• The computational graphs in TensorFlow help manage your model by reviewing the structure and determining if training occurs at the desired speeds.

• TensorFlow has features that allow you to develop highly scalable machine learning models.

Cons

• Other frameworks, such as PyTorch, can be more user-friendly and suitable for those new to machine learning.

• Model training in TensorFlow is less efficient than on comparable platforms.

• When using programming languages outside of Python for TensorFlow, they don’t always offer the same level of stability.

How to get started with TensorFlow

To use TensorFlow, you should first gain knowledge of a relevant programming language, such as Python, but you have other options as well, such as Java and Swift. To develop your programming skills, you can earn a degree in an area such as computer science or take a faster approach by completing a coding boot camp or online courses. From there, you can focus on developing skills specifically related to machine learning. Courses such as IBM's Machine Learning with Python or Deep Learning.AI’s Deep Learning Specialization courses are excellent options to build your machine learning skills.

Explore machine learning software and deep learning frameworks on Coursera

You’ll find many helpful options on Coursera to learn more about working with TensorFlow. For example, you can gain practical experience working with this platform while taking Building Deep Learning with Keras and TensorFlow, offered by IBM. You can also earn a DeepLearning.AI TensorFlow Developer Professional Certificate, which gives you an opportunity to build natural language processing systems, various types of neural networks, and computer vision applications.