TensorFlow is one of the best software libraries suited for AI and machine learning. It was developed by the Google Brain Team by partnering with Google’s machine intelligence research organization. It is very useful for various phases of the ML app development/deployment process, including the preparation of data. Apart from that, it also provides instant access to multiple tools and libraries for machine learning and deep learning in numerous languages. Depending on a genuine TensorFlow expert is also a great option as they would assist you in designing dataflow graphs.
Major TensorFlow benefits;
In the table given below, we shall focus on some of the advantages of TensorFlow:
| Benefits | Description |
| Flexibility | TensorFlow is not confined to a single device. It works smoothly on cellular devices as it functions effectively on other complicated machines. As the library is clearly defined, its deployment has no issues. |
| Open-Source Platform | An individual can access it at any point in time since it is available for free. This unique feature would enable a user to take advantage of this module from any corner of the world. |
| Graphs | Unlike other libraries, TensorFlow possesses great data visualization power. This makes it extremely simple to operate on neural networks. |
| Debugging | TensorFlow features a Tensorboard which ensures smooth debugging of nodes. As a result, they are very helpful in narrowing down the overhead of exploring the entire code. |
| Correlation | TensorFlow implements both GPU and CPU systems to ensure an effective functioning process. An individual could use the architecture according to their needs and preferences. A system would utilize GPU if it is not mentioned specifically. By doing so, the usage of memory will be less to a great extent. Owing to this ability, TensorFlow is regarded as a hardware acceleration library. |
| Compatibility | TensorFlow works well with programming languages such as Python, C++, and JavaScript. As a result, it would enable an individual to operate in an environment they are more convenient.
TensorFlow supports Python, C++, and JavaScript, allowing flexibility for developers. An NLP development company uses it to create precise and efficient language models. |
| Architectural Support | The architecture of TensorFlow would take advantage of TPU which ensures smooth computation when compared to GPU and CPU. Those models, which are created using TPU, can be positioned over clouds. In addition, it operates at a faster pace than that of GPU and CPU. |
| Library Management | With better support from Google, the TensorFlow library is updated on a regular basis. Apart from that, it has the capacity to show excellent performance as well. |
In the chart given below, you will gain a better understanding of the GitHub Star count of some of the best open-source deep learning frameworks. Just look at TensorFlow:
Real-world examples of TensorFlow in action
TensorFlow is an open-source machine library that is best suited to handle real-world business applications. The real-world examples of TensorFlow in action are listed below:
- Detecting Text and Categorizing
The discussion of some of the most popular use cases associated with deep learning cannot be complete without mentioning its text-based applications. Sentiment analysis, detecting fraud, and identifying potential threats are standard text-based applications related to deep learning. The presence of TensorFlow in sentiment analysis is very useful in social media marketing and customer relationship management. Likewise, fraud detection is also capable of supporting operations carried out in financial and insurance domains.
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The text-related application of TensorFlow also prioritizes the detection of language. For example, an individual will be able to locate support for multiple languages via Google Translate. The use cases of TensorFlow also feature summarization of text. Internet giant Google has discovered that a deep learning method known by the name sequence-to-sequence or in short S2S learning helps carry out text summarization. Creating news headlines is one of the applications of the S2S deep learning technique associated with TensorFlow. Besides, SmartReply, which can create email responses instantly is yet another best example of TensorFlow use cases related to text-related applications.
- Time Series Algorithms
The use of TensorFlow also happens with time series algorithms, as it helps analyze time series data. In certain circumstances, you may consider availing the services of a TensorFlow-certified developer to retrieve relevant statistics from the time series data. For example, TensorFlow is an ideal choice to forecast the stock market.
Today, some of the major platforms such as Netflix, Facebook, Amazon, and Google take advantage of deep learning to provide suggestions to users. An exact picture of the preferences and expectations of a customer can be provided by this module. For example, a TensorFlow deep learning framework permits recommendation engines to suggest certain TV shows or movies based on an individual’s habits. They also support use cases in various other sectors such as finance, governance, security, IoT, and accounting.
- Image Recognition
The use of image recognition contained in deep learning has a significant role in boosting the popularity of machine learning and enhanced learning. Some of the noteworthy users of image recognition applications consist of smartphone manufacturing firms, media, and telecom. Apart from that, image recognition can support usage that features photo clustering, image search, face recognition, and motion detection as well. The use cases of TensorFlow are best suited for applications such as automobile and healthcare industries. The presence of image recognition features in TensorFlow is very useful in getting familiar with the context along with image content. It is an ideal option for creating object recognition algorithms too.
See also: The ethics of using facial recognition technology
- Detecting Videos
Video detection is yet another notable and handy feature included in TensorFlow. The deep learning algorithms also assist in motion detection along with real-time thread detection in gaming and security. For the past several years, researchers have been actively involved in massive-scale datasets for video categorization. Youtube is one of the finest examples of datasets. These datasets are very useful in speeding up the process of research related to modeling of noisy-data, and transfer learning.
- Voice Recognition Applications
In addition to the above-mentioned, voice recognition algorithms are one of the best use cases of deep learning. TensorFlow can support the use of deep learning for various voice search applications. It is currently quite popular amongst manufacturers of smartphones and telecom giants. Some companies may even rely on a reliable TensorFlow developer to deal with sentiment analysis in CRS-based applications. Apart from that, they are best suited for the automobile and aviation industry, as they help in tracking errors at an early stage including unusual noise of engines.
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- Recommender Systems
Recommender Systems can provide recommendations in a personalized format to individuals based on their expectations. TensorFlow provides numerous libraries along with modules that would assist you in designing recommender systems. Some of them include TensorFlow Recommenders for building and gauging models, and TensorFlow Serving for employing models. In addition, TensorFlow Ranking to learn how to rank items is also available. Content recommendation, product recommendation, and ad recommendation are some of the most popular recommender systems use cases of TensorFlow.
The use cases of TensorFlow highlight the fact that it is one of the best tools for software developers who are focusing on deep learning algorithms. Being a highly advanced subset of AI, deep learning is very helpful in developing the upcoming generation of smart applications as well as systems. The use of TensorFlow within deep learning would ease the ML development/deployment process while offering maximum scalability for development along with visualization.
