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The early applications of machine learning and AI have changed the traditional notions about leveraging technological advancements for growth. Machine learning is one of the subsets of AI which focuses on creating computer systems capable of learning like humans. Interestingly, the developments in AI and machine learning have introduced advanced algorithms and other subsets of AI, such as computer vision and deep learning. 

The popularity of deep learning has led to an exponential rise in algorithms or Neural Networks which simulate the biological nervous systems. You can prepare such algorithms by using frameworks like TensorFlow. Therefore, the use of TensorFlow has grabbed the attention of thousands of professionals in the AI and deep learning community. What is TensorFlow, and how does it support deep learning? Do you know about the real-world use cases of TensorFlow? Let us find the answers to these questions in the following post.

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Brief Overview of the Origins of TensorFlow

Before you find information about the top TensorFlow use cases, it is important to learn about the origins of TensorFlow. Deep learning has become a popular subset of AI with a broad range of applications across different industries. The neural network algorithms in deep learning feature similarities to the human brain in terms of functionalities for processing information. The deep learning algorithm could help in identifying every data about the inputs and learning new patterns in the data.

You can find different libraries suited for deep learning, and TensorFlow is one of the most popular options among them. It is an open-source AI library that utilizes data flow graphs for building models. TensorFlow can help in creating large-scale neural networks featuring multiple layers. The most common TensorFlow uses to focus on addressing deep learning or machine learning issues such as perception, classification, discovery, creation, understanding, and prediction. TensorFlow was created by the Google Brain team for internal applications in research and production.

The initial version of TensorFlow was released in 2015, and the updated version was released in September 2019 as TensorFlow 2.0. The origins of TensorFlow can be credited to the closed-source DistBelief framework on Google. DistBelief served as the playground for experiments with deep learning implementations for recommendation engines, advanced image and speech recognition, predictive analytics, and natural language processing. 

You can find answers to “Do professionals use TensorFlow?” by reflecting on the differences between TensorFlow and DistBelief. TensorFlow has been designed as a general machine learning framework with support for faster configuration and operations with high-level APIs.

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Value of TensorFlow Architecture 

TensorFlow helps developers in creating dataflow graphs or structures which describe the movement of data through graphs or collections of processing nodes. Every node in the graph serves as the representation of mathematical operations, and each connection between the nodes is a tensor. 

The popularity of TensorFlow usage examples has been rising higher as the architecture of TensorFlow delivers promising advantages. As a matter of fact, some of the big brands have turned towards TensorFlow architecture for machine learning applications. TensorFlow stands out as a popular deep-learning framework due to the assurance of convenient usage.

The TensorFlow library features a combination of different APIs, which can help you in creating deep learning architectures. You can create Convolution Neural Networks or CNNs and Recurrent Neural Networks or RNNs. As the architecture of TensorFlow relies on graph computation, it helps the developer in imagining the structures of the neural network with visualization capabilities through TensorBoard.  

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Why Should You Use TensorFlow?

The next important concern on your mind right now must be the reasons for using TensorFlow. It is evident that TensorFlow provides a unique architecture for a deep learning framework through graph computation modeling. However, the best use cases of TensorFlow do not rely on architectural flexibility alone. On the contrary, you can find many other benefits with TensorFlow for deep learning.

For example, TensorFlow is an open-source system and could facilitate the development of any type of deep learning use case. Another prominent trait of TensorFlow points to the advantage of data visualization, which also helps in debugging nodes. As a result, you don’t have to review the whole code completely to resolve the issues with neural networks. 

The use of TensorFlow is scalable, which allows performing any type of operation on the platform. Graphical representation offers the ease of developing any type of system by utilizing TensorFlow. It also offers the advantages of compatibility with different languages such as Python, C++, Ruby, C#, and JavaScript.

As a result, it could enable users to work in environments that they are comfortable with. Another important highlight for using TensorFlow points to the power of TPUs in its architecture. TPUs can guarantee faster computations as compared to CPUs and GPUs. At the same time, TensorFlow also features parallelism of work models with different distributed strategies. 

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Top Use Cases of TensorFlow

The architecture and features of TensorFlow provide a clear impression of reasons to choose TensorFlow right now. On top of it, TensorFlow was created on the foundations of the objectives for resolving machine learning and deep learning problems. Interestingly, the most popular use cases of TensorFlow span across different industries, including e-commerce, social networks, and healthcare.

TensorFlow is open-source and has a large community, thereby showcasing its power for solving real-world business applications. The business applications of TensorFlow include image analysis, creation of intelligent chatbots, natural language processing, and data generation. Here is an outline of the top five use cases of TensorFlow. 

top tensorflow use cases

  • Text Detection and Classification

The outline of most popular use cases of deep learning would be incomplete without referring to text-based applications. Some of the common text-based applications of deep learning include sentiment analysis, fraud detection, and threat detection. The collection of TensorFlow usage examples in sentiment analysis could help in social media marketing and customer relationship management. Similarly, fraud detection could support operations in the financial services and insurance sectors.

The text-based application of TensorFlow also focuses on language detection. For instance, you can find support for more than 100 languages on Google Translate. The use cases of TensorFlow also include text summarization. Google has identified that a deep learning technique known as sequence-to-sequence or S2S learning can help with text summarization. 

The practical applications of S2S deep learning technique with TensorFlow would focus on generating news headlines. Another example of TensorFlow use cases for text-based applications is SmartReply, which could generate e-mail responses automatically. 

The use cases of TensorFlow also include time series algorithms, which help in analysis of time series data. The top TensorFlow use cases in time series algorithms help in obtaining meaningful statistics from time series data. For instance, TensorFlow can use time series data to predict the stock market. In addition, the deep learning framework can help in generating forecasts for non-specific time periods alongside generating alternative versions of time series.

You can find the most common applications of time series algorithms created with TensorFlow in recommendation engines. Some of the popular platforms, such as Netflix, Amazon, Facebook, and Google, utilize deep learning to offer recommendations to users. The deep learning algorithm supports analysis of customer activity alongside comparing the data to that of millions of other users. 

As a result, it could provide an accurate estimate of the customer’s most favorable preferences, for example. TensorFlow deep learning framework could empower recommendation engines to recommend movies or TV shows based on your watching habits. 

Interestingly, the use of TensorFlow for creating time series algorithms can also support similar use cases in other industries. Deep learning can help finance, IoT, governance, security, and accounting with the advantages of predictive analytics, resource planning, and risk identification.

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The use cases of image recognition in deep learning have a major role in increasing the popularity of machine learning and deep learning. Some of the notable users of image recognition applications include telecom, smartphone manufacturers, and media. In addition, image recognition also supports the use cases which involve face recognition, photo clustering, image search, and machine vision and motion detection. 

The best use cases of TensorFlow in image recognition could also help industries such as healthcare, aviation, and automotive. For example, the applications of image recognition could help in recognizing and identifying people alongside objects in certain images. On top of it, image recognition use cases of TensorFlow could also help in understanding the context alongside content of the image.

TensorFlow is an ideal choice for developing image or object recognition algorithms. It could support the classification and identification of arbitrary objects in large images. In addition, such abilities of TensorFlow can help in designing engineering applications for identifying the ideal shapes for modeling 2D images or 3D space reconstruction. 

The most popular use cases of TensorFlow in image recognition also point to the Deep Face algorithm of Facebook, which helps in photo tagging. Deep learning algorithms utilize TensorFlow for analysis of multiple images of dogs and identify a specific breed of dog based on general traits of the breed.

The healthcare industry can utilize image recognition for effective and faster diagnosis through review of scans. Deep learning algorithms could process more information and identify more patterns in comparison to humans.

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Another noticeable entry among the use cases of TensorFlow would draw attention towards video detection. Deep learning algorithms help with motion detection alongside real-time thread detection in UX/UI, gaming, and security. The responses to “Do professionals use TensorFlow?” also focus on the use of deep learning for video detection in airport security.

As a matter of fact, researchers have been working on large-scale datasets for video classification. One of the examples of such datasets is the YouTube-8M. It helps in speeding up the pace of research regarding large-scale video comprehension, noisy data modeling, domain adaptation approaches, transfer learning, and representation learning. 

The most noticeable example of implementing TensorFlow in deep learning applications points at NASA. The space research organization has developed a deep learning system with TensorFlow for facilitating object clustering of asteroids alongside orbit classification. NASA aims to improve the classification and prediction of Near Earth Objects or NEOs.

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  • Voice Recognition Applications 

The last addition among the examples of TensorFlow uses in deep learning would point to voice recognition applications. Voice recognition algorithms are one of the most popular use cases of deep learning. As a matter of fact, neural networks with the right input data feeds could understand audio signals with ease.

You can come across examples of voice recognition in IoT applications alongside UX/UI design, automotive and security industries. For example, voice-enabled passwords and commands for starting vehicles show the potential of deep learning. 

TensorFlow could also support the use of deep learning for voice search applications, which are popular among telecom giants and smartphone manufacturers. On top of it, best use cases of TensorFlow in deep learning for voice recognition also help with sentiment analysis in CRM applications. In the automotive and aviation sector, voice recognition use cases of TensorFlow could serve lifesaving outcomes by earlier flaw detection, such as engine noise.

The common use cases of voice recognition also include language understanding alongside speech-to-text applications. Such types of applications could take snippets of audio from larger files and transcribe them in the form of text. One of the use cases of TensorFlow for voice recognition would be automated customer service agents that guide customers toward relevant information. 

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Conclusion 

The use cases of TensorFlow showcase that it is a powerful tool for developers working on deep learning algorithms. Deep learning is an advanced subset of AI and could help in creating the next generation of smart applications and systems. However, the use of TensorFlow in deep learning makes the process easier, with flexibility for development and visualization.

You can learn more about the fundamentals of TensorFlow and its features to understand how it is a powerful tool for the AI ecosystem. Many organizations have embraced TensorFlow in their digital transformation initiatives with value-based use cases. Explore the different examples of TensorFlow use cases and uncover more details about the future of deep learning right now.

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