Nowadays, lots of people are excited about Machine Learning as majority business operations have imbibed this technology and it has proved to be quite essential. The term ‘Machine Learning’ was coined at IBM in 1959, and the technology progressed as a subset of Artificial Intelligence. Machine learning is not just glorified statistics, but it’s a complex discipline. Thanks to the machine learning frameworks such as Google’s TensorFlow as it has simplified the process of analysing predictions, training models, acquiring data and refining future results.
TensorFlow, an open-source software library created by the Google Brain team for numerical computation; it works in similar ways as we humans make use of reasoning and observation skills to learn. TensorFlow amalgamates deep learning, machine learning and algorithms to make machine learning faster and easier. This framework makes use of Python to offer a seamless front-end API for building apps on the other hand use C++ for high-performance. Google itself is using TensorFlow for its best-known software.
TensorFlow is not just a library; it is a set of APIs. It is convenient as programmers don’t want to hand over the code to the low-level algorithms as each time they are required to use it in a codebase. TensorFlow has given us a choice, with TensorFlow core the programmers have full control over the models they built. Along with that TensorFlow provides a visual learning tool called TensorBoard that provides visualisations in real-time of your machine learning work.
Key Highlights of TensorFlow
- Abstraction
- Eager Execution Mode
- Google’s Online Hub
Abstraction is considerably in favour of ML and AI development. Instead of dealing with every micro detail of identifying a proper approach or incorporating algorithms to hitch the result of one function, the programmers can exclusively focus on the overall logic of the application.
Eager execution mode is another convenience to the developers. This method let them modify and evaluate each graph operation instead of developing the whole graph as a single object at once. TensorBoard visualisation suite allows programmers to inspect and profile in a similar way graph operates an interactive dashboard.
It has not just simplified the development process, but this flexible architecture allows easy deployment of computation across a variety of platforms to make it more efficient and use TPU for amplified performance and in-browser incarnations for sharing developed models.
TensorFlow is Brining Machine Learning to Everyone
TensorFlow holds the capability to solve a wider range of machine learning problems as it allows programmers to build deep neural networks and run them across thousands of computers in data centres. TensorFlow is an open-source machine learning framework, and it is considered as the second generation machine learning system, and it is widely used in image recognition, where deep convolutional are leveraged to convey images with high accuracy. The latest video messaging application – Tribe makes use of natural language processing techniques along with make use of TensorFlow machine learning API to scan audio in video messages.
TensorFlow is widely used to prevent blindness by assisting doctors to combat diabetic retinopathy. The Journal of American Medical Association stated that computer vision model seems better than a median ophthalmologist. Indian and US doctors together carried out this project with a database of 1,28,000 that were exclusively evaluated by a team of 7 ophthalmologists from a panel of 54 ophthalmologists. The entire dataset was used to train and detect referable diabetic retinopathy.
What Can You Do With TensorFlow?
Machine learning is indeed capable of handling unstructured data and large-scale problems that seems like impractical to code by the rule-based deterministic approach. Almost all the amenities of modern living are power-driven by data, from iPhone’s Siri to the recommendations on your Netflix application. Businesses are partnering with machine learning development company to utilize their expertise in making the data better, whether it’s internal process improvement or if it’s the case of boosting conversions through predictive analytics. Businesses are looking for modern approaches to enhance their customer service through personalised customer journeys – chatbots. TensorFlow is specifically used for: Classification, creation, prediction, perception, understanding and discovering.
TensorFlow Use Cases for Real-world Applications
1. Voice/Sound Recognition
TensorFlow is widely used in Sound based applications.
- Voice search – Telecom sector, Handset Manufacturers
- Voice recognition –IoT, Automotive, Security and UX/UI
- Sentiment Analysis –CRM
- Flaw Detection –mostly used in Automotive and Aviation
Regarding common use cases, we are all familiar with voice-search and voice-activated assistants with the new wide spreading smartphones such as Apple’s Siri, Google Now for Android and Microsoft Cortana for Windows Phone. Apple’s Siri, Google and Microsoft’s Cortana for Android and windows phone are voice-search and voice-activated assistants extensively used in smartphones. Speech to text application is another common use to determine snippets of sound in greater audio files. TensorFlow algorithms perform as the customer service agents and provide customers with the relevant information they need, faster than the humans.
2. Text-Based Applications
Further favourite uses of TensorFlow is a text-based application such as threat detection, sentimental analysis and fraud detection. Google translate is one of the favourite examples as it supports 100 languages to translate from one to another. SmartReplay is another popular framework as it automatically generates email responses with a technique called sequence-to-sequence learning.
3. Image Recognition
Image Search, Face Recognition, Photo Clustering, Machine Vision are used in automotive, aviation and healthcare industries to identify people and objects understanding the content and context. TensorFlow’s object recognition algorithm is popular in modelling purposes and social networks for photo tagging. Healthcare Industry has started implementing TensorFlow algorithms to identify more information and spot of patterns than normal human counterparts. Computer holds more capability to review, scan and spot more diseases than humans.
4. Time Series
TensorFlow Time Series algorithms are used to extract significant statistics. It allows to forecast non-specific time periods to produce alternative versions of the time series. Recommendation is a common use case for time series. Amazon, Google, Facebook and Netflix analyse customer spent time period and compare it to the millions to conclude what the customer might like to purchase or watch. These recommendations are getting even smarter in the field of Government, Security, IoT, Finance, and Accounting with Predictive Analysis, Risk Detections, and Resource Planning.
5. Video Detection
TensorFlow neural networks work perfectly fine in motion detection, real-time thread detection in security, gaming, UX/UI fields and at airports. Recently, Universities are studying video classification datasets to accelerate large-scale video understanding, noisy data modelling, transfer learning, and representation learning, for video.
As TensorFlow is an open source library, so it is possible that more businesses will come up with more innovative ideas to contribute to machine learning technology and influence one another.
Wrapping Up
TensorFlow has successfully made its wave in the IT sector and that too with a sound reason. Machine learning always seems complex at first, and it has never been this easier, but the more you understand this framework, the easier you will find it to adopt.
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To sum up, if you are wondering what TensorFlow is capable of and what more could be done using this open-source library, hire TensorFlow developer from us to explore more possibilities and run your application on almost any suitable target like a cluster in iOS, Android and cloud device.
