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Deep-learning

Powerful Proven
Deep-learning technology

Artificial Intelligence and image recognition tool search, verify billions of images

Deep-learning

Benefits

Artificial-Intelligence

Artificial Intelligence

AI Platform combines deep learning and machine learning with blazing performance. Latest hardware architecture GPU, CPU, and interconnect technology enable deep learning solution in enterprize level.

Pattern-recognition-analysis

Pattern recognition analysis

With large data support, powerful pattern recognition analysis can be performed in breaking speed. Processing large data set and effective modeling capability open to opportunity to solve many challenges and problem when company perform pattern recognition analysis because of feasible business needs effectively.

Capabilities

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Abnomaly detection automation

Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting Eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy.

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Deep learning solution

Deep learning solution trains big data and analyze through deep learning algorithms, then users enable to create and manage their custom classifiers through a web app. By entering API key, users can use our GUI to seamlessly create, retrain, and delete custom classifiers associated with their API key without needing to go through the hassle of forming complex HTTP requests. service will soon be added to deep learning solution.

Technoloogy

Convolutional Neural network

A Convolutional Neural Network is comprised of one or more convolutional layers and then followed by one or more fully connected layers as in a standard multilayer neural network. The architecture of a CNN is designed to take advantage of the 2D structure of an input image. This is achieved with local connections and tied weights followed by some form of pooling which results in translation invariant features.

Recurrent Neural Network

Recurrent Neural Networks were created in the 1980’s but have just been recently gaining popularity from advances to the networks designs and increased computational power from graphic processing units.

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Convolutional Neural network

A Convolutional Neural Network is comprised of one or more convolutional layers and then followed by one or more fully connected layers as in a standard multilayer neural network. The architecture of a CNN is designed to take advantage of the 2D structure of an input image. This is achieved with local connections and tied weights followed by some form of pooling which results in translation invariant features.

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Recurrent Neural Network

Recurrent Neural Networks were created in the 1980’s but have just been recently gaining popularity from advances to the networks designs and increased computational power from graphic processing units.

Resources

  • All the resources used on beeHive are optimized for scale-out by increasing the number of linked servers using beeCloud to improve processing power, and can be auto scaled. In addition, it does not require any installation to process large amounts of data, thus reducing the burden on users to store and manage data directly in their workspace.
  • Link to resources