Which of the following frameworks is NOT included with AWS Deep Learning AMIs?

Which of the following frameworks is NOT included with AWS Deep Learning AMIs?

• A. Microsoft Cognitive Toolkit (CNTK)
• B. Apache MXNet
• C. TensorFlow
• D. Scikit-learn

Answer: (D)

AWS Deep Learning AMIs are specifically designed to provide a comprehensive environment for developing deep learning models. They come pre-installed with a variety of popular deep learning frameworks to facilitate model building and training. The frameworks included in AWS Deep Learning AMIs, such as Microsoft Cognitive Toolkit (CNTK), Apache MXNet, and TensorFlow, are tailored for deep learning applications, offering capabilities for building and training complex neural networks. Each of these frameworks is optimized for performance and supports GPU acceleration, making them suitable for high-performance computing tasks typically associated with deep learning. On the other hand, Scikit-learn is primarily focused on traditional machine learning tasks rather than deep learning. It includes a range of algorithms for tasks such as classification, regression, clustering, and dimensionality reduction, but it does not specialize in deep learning frameworks or capabilities. While Scikit-learn is an excellent library for general machine learning practices, it lacks the deep neural network structures and functionalities provided by the other three options listed. Therefore, it is not included with AWS Deep Learning AMIs, which focus primarily on deep learning frameworks.

AWS Deep Learning AMIs are specifically designed to provide a comprehensive environment for developing deep learning models. They come pre-installed with a variety of popular deep learning frameworks to facilitate model building and training.

The frameworks included in AWS Deep Learning AMIs, such as Microsoft Cognitive Toolkit (CNTK), Apache MXNet, and TensorFlow, are tailored for deep learning applications, offering capabilities for building and training complex neural networks. Each of these frameworks is optimized for performance and supports GPU acceleration, making them suitable for high-performance computing tasks typically associated with deep learning.

On the other hand, Scikit-learn is primarily focused on traditional machine learning tasks rather than deep learning. It includes a range of algorithms for tasks such as classification, regression, clustering, and dimensionality reduction, but it does not specialize in deep learning frameworks or capabilities. While Scikit-learn is an excellent library for general machine learning practices, it lacks the deep neural network structures and functionalities provided by the other three options listed. Therefore, it is not included with AWS Deep Learning AMIs, which focus primarily on deep learning frameworks.