Let us now see some Tensorflow applications.
1. Image Recognition
It’s one of the most popular Uses of TensorFlow. It is used by Mobile companies, social media, and other telecom houses. Image recognition consists of pixel and pattern matching to identify the image and its parts. Image recognition consists of the following steps:
- Find out the features of pixel– Each image is a container of pixels which in turn are the combination of numbers. These numbers represent the color depth.
- Equip an image for training– Categorize the images under a different section to train a model. For example, classify an image as ‘car’, ‘bike’ etc for better understanding. For better performance, train a model using many images.
- Train the model to categorize images– With the help of various images, train a network that can produce a label as an output from the given image as an input.
- Provide an unknown input– Test the model by providing it a new image that can have a classification in any of the set categories.
Image recognition finds its application in many domains including health care systems, banking systems, educational institutions, etc.
2. Voice Recognition
TensorFlow has significant use in voice recognition systems like Telecom, Mobile companies, security systems, search engines, etc. It uses the voice recognition systems for giving commands, performing operations, and giving inputs without using keyboards, mouse. It is done using Automatic speech recognition which is trained using TensorFlow. These systems convert the human voice into text or computer-understandable code by digitizing it.
The systems like Bluetooth, digital assistants, google voice are based on models trained using TensorFlow. Customer relationship management (CRM) for client-based systems is also built using a voice recognition technique in TensorFlow.
3. Video Detection
With increased technology, companies and businesses look forward to more secure and optimized systems. Hence, the motion detection is used widely at airport security checks, gaming controls, and movement detection. Here uses of TensorFlow include self-driving car systems, automation, and many automotive machines.
To build a video detection environment, it follows the following steps:
- Setup the environment
- Provide the metadata and pictures
- Train the model
- Modify it to TensorFlow Lite
- Test the model
It defines these highly advanced systems using the Object Detection API which takes the support of TensorFlow.
4. Text-based applications
The text messages, reactions, comments, tweets, stock results, etc are a means of data. This processing of data is done using TensorFlow for the analysis purpose and reaching the expected sales. We do it using different techniques like sentiment analysis, a bag of words, and many more. This can help to find out the risk associated with any organization by decoding the words used in texts.
Furthermore, Google uses it for translating texts from one language to over 100 languages.