How to Use TensorFlow The Ultimate Uses of TensorFlow

How to Use TensorFlow – The Ultimate Uses of TensorFlow

In case you didn’t know yet, the main software tool of deep learning is the TensorFlow. This is an open source artificial intelligence library, which makes use of data flow graphs to create models. It enables developers to make big scale neutral networks with different layers. Mainly, TensorFlow is utilized for Creation, Prediction, Discovering, Understanding, Perception, and Classification.

For Video Detection

TensorFlow neutral networks work on video data. This is mainly utilized in Motion Detection, security, Real Time Thread Detection in Gaming and more. Lately, colleges are conducting on Large scale Video classification datasets such as YouTube-8M striving to boost research on big-scale video understanding, noisy data modeling, representation learning.

Time Series

On the other hand, TensorFlows Time Series are utilized for assessing time series data to extract relevant statistics. They enable forecasting non-specific time periods aside from generating alternative versions of time series. The most typical use for a case for this is Recommendation. You have probably heard of this use of Netflix, Facebook, Google and Amazon where they assess customer activity and compare to other users to identify what the customer might want to buy or watch.

Image Recognition

The majority used by Handset Manufacturers, Telecom or Social Media; Photo Clustering, Machine Vision, Motion Detection, Image Search and Face Recognition can be utilized also in Healthcare, Aviation, and Automotive industries. This aims to determine and recognize objects and people in images and understanding context and content.

Text Based Application

Other famous uses of TensorFlow are some text based applications like sentimental analysis, Threat Detection, and Fraud Detection. Language Detection is one of the most sought after uses of a text based application.

Sound/Voice Recognition

One of the most popular uses of a Tensorflow is the sound based applications. With the use of right data feed, neural networks are able to understand audio signals, and this could be:

  • Flaw detection – Mostly utilized in Aviation and Automotive
  • Sentiment Analysis – Mostly utilized in CRM
  • Voice Search – Mostly utilized in Handset, Telecom Manufacturers
  • Voice Recognition – Mostly utilized in Automotive, IoT, UX/UI and Security

Apart from the common use cases, perhaps you are familiar with voice activated and voice-search assistants along with the new wide spreading mobile phones such as Google Now for Android, Siri of Apple and the Windows Phone from Microsoft Cortana.

Bear in mind that language understanding is another typical use for Voice Recognition. Apart from that, speech-to-text applications could be utilized to determine snippets of sound in audio files and copy the spoken word as text. Meanwhile, sound based applications could be utilized in CRM as well. A use case scenario may be TensorFlow algorithm standing in for a customer service agents, and route the customer to essential information they want, and quicker than the agents.

Take note that as TensorFlow is one of those open source libraries, you will see more innovative uses cases later on that will surely affect one another and add to Machine Learning technology.

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