Data visualization

TensorFlow has great computational graph visualizations. It also allows easy debugging of nodes with the help of TensorBoard. This reduces the effort of visiting the whole code and effectively resolves the neural network.

Keras friendly

TensorFlow has compatibility with Keras. Its users can code some high-level functionality sections in it. Keras provides system-specific functionality to TensorFlow, such as pipelining, estimators, and eager execution.

With its characteristic of being deployed on every machine and the graphical representation of a model, TensorFlow allows its users to develop any kind of system using TensorFlow.

It is compatible with many languages, including C++, JavaScript, Python, C#, Ruby, and Swift. The language compatibility allows users to work in environments they are comfortable.

Due to the parallelism of work models, TensorFlow find its use as a hardware acceleration library. It uses different distribution strategies in GPU and CPU systems.

Deep learning uses TensorFlow for its development as it allows the building of neural networks with the help of graphs that represent operations as nodes.

Voice and Sound recognition applications are the most well-known use cases of deep learning. If the neural networks have the proper input data feed, neural networks are capable of understanding audio signals.

Text-based applications are popular use cases of deep learning. Common text-based applications include sentiment analysis (for CRM and social media), threat detection (for social media and government), and fraud detection (insurance and finance). Furthermore, language detection and text summarization are the other most popular uses of text-based applications. Our TensorFlow with GPU servers can run these applications well.

Social Media, Telecom, and Handset Manufacturers mostly use image recognition. Image recognition is used for: face recognition, image search, motion detection, machine vision, and photo clustering. It also finds its use in the automotive, aviation, and healthcare industries. For example, businesses use image recognition to recognize and identify people and objects in images. By using the TensorFlow with GPU servers, users can implement deep neural networks for use in those image recognition tasks.

Deep learning uses time-series algorithms for analyzing data to extract meaningful statistics. For example, it can use time series to predict the stock market. So, deep learning is used to forecast non-specific periods in addition to generating alternative versions of the time series. Deep-learning time series is used in finance, accounting, government, security, and the Internet of Things with risk detections, predictive analysis, and enterprise/resource Planning. All these use cases could rely on the high-performance computing in the TensorFlow with GPU server.

Clients also opt for the TensorFlow with GPU server for video detection, such as in motion detection, real-time threat detection in gaming, security, airports, and user experience/ user interface (UX/UI) fields. Some researchers are working on large-scale video classification datasets, such as YouTube, to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.