TensorFlow is one of the most widely and well known tools for machine learning and with its various features, it maintains its versatility to operate in different use cases.
It holds the capability of training a model for systems with the use of graphs based on each operational node.
In real-time, Google uses TensorFlow to upgrade the services it provides such as Gmail, Google search engine, image captioning, and many more. It uses TensorFlow in various domains about the requirements and its usage.
Some uses of TensorFlow include:
Voice Recognition
This involves converting human sound signals into words or instructions. It is an important feature that TensorFlow provides. TensorFlow has significant use in voice recognition systems like Mobile companies, security systems, search engines etc.
It utilizes voice recognition systems for giving commands, performing operations and giving inputs without the use of input hardware like mouse or keyboards. Automatic speech recognition is made possible because these systems have been trained to automatically recognize speech and convert human voice into text or computer understandable code.
Customer relationship management (CRM) for client-based systems are built using a voice recognition technique in TensorFlow. Also, bluetooth, digital assistants, google voice are based models trained using TensorFlow.
Image Recognition
TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. With relatively same images, it will be easy to implement this logic for security purposes.
Image recognition refers to the task of inputting an image into a neural network and having it output some kind of label for that image. The label that the network outputs will correspond to a predefined class.
There can be multiple classes that the image can be labeled as, or just one. If there is a single class, the term “recognition” is often applied, whereas a multi-class recognition task is often called “classification”.
In order to perform image recognition / classification, the neural network must carry out the following:
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Feature recognition (or feature extraction): is the process of pulling the relevant features out from an input image so that these features can be analyzed. Many images contain annotations or metadata about the image that helps the network find the relevant features.
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Equip an image for training: Categorize the images under a different section to train a model. For example, classify an image as ācarā, ābikeā etc, for better understanding. For better performance, train a model using many images.
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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.
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Provide an unknown input: Test the model by providing it a new image that can have a classification in any of the set categories.
Text-based applications
Text-based applications are very popular use cases of TensorFlow. Some of their use cases include:
- Text-based applications such as sentiment analysis (for customer relationship management (CRM) and social media), threat detection (for social media and government), and fraud detection (insurance and finance) are done using TensorFlow.
- Tensorflow’s text summarization. For example, Google also found out that summarization can be made using a deep learning technique called sequence-to-sequence (S2S) learning. Indeed, this S2S deep learning technique can be used to produce headlines for news articles.
- SmartReply is another Google use case, which automatically generates email responses.
- 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.
- Google also uses tensorflow for translating texts from one language to over 100 languages. This is achieved 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.
Video Detection
TensorFlow algorithms can be used for video detection. So, this is mainly used in motion detection, real-time thread detection in gaming, security, airports and user experience / user interface (UX/UI) fields. 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.
A couple of researchers are working on large-scale video classification datasets such as YouTube-8M to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.