TensorFlow Applications in Real Life
Tensorflow is, without doubt, the most used platform for machine learning and deep learning. It includes the various features to use in various domains and applications. Tensorflow lets designers make models and perform computations to complete. Each node in the graph illustrates a mathematical function and each association conveys data. In this article, we will see the various applications of tensorflow in real life.
TensorFlow Applications in Real Life
1. Voice and Sound recognition
Voice and Sound recognition applications are the considerable well-known application of deep learning If the neural networks have the appropriate input data feed, neural networks are qualified for comprehending audio signals.
For example:
- Voice recognition
- The industry of telecom uses Voice search.
- Sentiment Analysis maintains and manages the relationship between customer and client.
- Flaw Detection in engines
- voice search system in Apple’s Siri, Microsoft Cortana, etc
Similarly, speech-to-text applications can be utilized to select parts of sound in more significant audio files and convert the spoken word into text.
In customer and client relationships there are many applications in deep learning.
2. Text-Based Applications
Text-based applications are used widely in deep learning to achieve many advantages and features.
For example:
Sentiment analysis is used for consumer connection management and threat detection that happens in social media and by society and fake and scam detection.
Google Translate, whenever we have any sentence or word that we want to translate to our local language we can do it easily using google translate which supports over 100 languages.
Text summarization by using deep learning methods helps in news classification.
3. Sequence-to-Sequence models and human language
Sequence-to-Sequence (Seq2Seq) models employ neural networks that occur concurrently as a step up by providing lots of sentence duos during model training so that we can develop a single sentence from another sentence. These sentence duos can be anything.
The model is made in such a way that for example, when you have sentences from two other speeches, the model can be employed for translations.
When it is a text message between individuals, the model can be utilized for chatbots.
One fascinating fact is that we just need to provide a basic word sequence and we can get a word series product with mostly correct grammar maybe not excellent, but at least comprehensible.
This means that seq2seq models can understand the language model from the training model and implicitly understand it during movement, which usually is not the situation in traditional NLP because the language model needs to be explicitly instructed.
What is language modeling?
Language modeling is the chance or probability a word is going to occur based on the prior arrangement of words and is the solution to many fascinating situations such as:
- Speech recognition
- Virtual assistants that we use such as Alexa
- Machine translation
- Collection image captioning
4. Image Recognition
Image recognition and detection are one the most commonly used application in deep learning using TensorFlow.
One of the numerous applications for deep learning is to help devices remember images and produce a noteworthy observation in a way that is helpful only to humans.
This implies having a computer identifying the colored specks in an image and assembling them to become a whole meal or dessert.
To match precision, an AI can trains on data in such a way that accurate answers are already known. So how it happens is the neural network will comprehend to remember and recognize a photo, examine if it is giving accurate output, and improve its ability to make sense of the colored clusters present in the picture. But true performance is giving AI a new dataset to see how it functions.
It extensively uses social media platforms, as image recognition helps in face recognition, image search, detection of motion, and image clustering
Image recognition is also used in automotive, flight, and healthcare ambitions to determine forms for modeling purposes.
Now a question might arise how does image recognition by deep learning be of any use to us?
The advantage of using TensorFlow for image recognition algorithms is that it allows to recognize random entities within more extensive pictures.
Deep learning uses TensorFlow for studying thousands of photos of cats, for instance, so the deep learning algorithm can be used to understand to recognize a cat because this algorithm uses the idea to locate broad elements of items, creatures, or people. It also used in healthcare industries to use deep learning algorithms are to examine scans and locate more diseases than doctors or physicians.
5. Prediction
Deep learning is also used in the prediction of time and it uses time series to signify the stock market.
That is as soon as we watch a movie or buy some online products we get a recommendation from Amazon, Google, Facebook, and Netflix. To study customer action and resemble it to other users it uses deep learning algorithm to decide what the client might like to buy or look at. For instance, it also suggests TV series or films that other individuals like based on the TV shows or films you already observed.
6. Video Detection
Deep learning algorithms are also utilized for video detection and it uses gaming, security luggage checkups on a conveyer belt in an airport.
Currently, serious research done on large-scale videotape classification datasets is to increase research on video performance, expression learning, loud data modeling, and other approaches for video. .
Summary
With the benefit of TensorFlow, Machine Learning has already reached the peaks that we earlier considered to be impossible. There is barely a part in our life where a technology that is created with the usefulness of this framework has no influence.
From healthcare to the e-commerce industry, the applications of TensorFlow have broadened the area of artificial intelligence in every demand to improve our understanding.