{"id":352,"date":"2023-10-03T06:16:54","date_gmt":"2023-10-03T06:16:54","guid":{"rendered":"http:\/\/willdickey.me\/?p=352"},"modified":"2023-10-06T16:24:02","modified_gmt":"2023-10-06T16:24:02","slug":"top-5-use-cases-of-tensorflow","status":"publish","type":"post","link":"http:\/\/willdickey.me\/index.php\/2023\/10\/03\/top-5-use-cases-of-tensorflow\/","title":{"rendered":"Top 5 Use Cases of TensorFlow"},"content":{"rendered":"

The early applications of machine learning and AI have changed the traditional notions about leveraging technological advancements for growth. Machine learning<\/a> is one of the subsets of AI<\/a> 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.\u00a0<\/span><\/p>\n

The popularity of deep learning<\/a> 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<\/a> 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.<\/p>\n

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Brief Overview of the Origins of TensorFlow<\/b><\/h2>\n

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.<\/p>\n

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.<\/p>\n

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.\u00a0<\/span><\/p>\n

You can find answers to \u201cDo professionals use TensorFlow?\u201d 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.<\/p>\n

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Value of TensorFlow Architecture\u00a0<\/b><\/h3>\n

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.\u00a0<\/span><\/p>\n

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.<\/p>\n

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.\u00a0\u00a0<\/span><\/p>\n

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Why Should You Use TensorFlow?<\/b><\/h3>\n

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.<\/p>\n

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\u2019t have to review the whole code completely to resolve the issues with neural networks.\u00a0<\/span><\/p>\n

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.<\/p>\n

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.\u00a0<\/span><\/p>\n

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Top Use Cases of TensorFlow<\/b><\/h3>\n

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.<\/p>\n

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.\u00a0<\/span><\/p>\n

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