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Microsoft Cognitive Toolkit

Introducing Microsoft Cognitive Toolkit (CNTK) ๐Ÿš€ Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit designed for commercial-grade distributed deep learning. While no longer actively developed, CNTK 2.7 marks its final major release. Here are some key features: ๐Ÿ“ˆ Describes neural networks as a series of computational steps via a directed graph. ๐Ÿ”€ Easily realize and combine popular model types like feed-forward DNNs, CNNs, and RNNs/LSTMs. โš™๏ธ Implements SGD learning with automatic differentiation and parallelization across multiple GPUs and servers. ๐Ÿ”ง Can be included as a library in Python, C#, or C++ programs, or used as a standalone machine-learning tool through its model description language, BrainScript. ๐Ÿ’ป Supports 64-bit Linux or 64-bit Windows operating systems. ๐Ÿ”„ One of the first deep-learning toolkits to support the ONNX format for framework interoperability and shared optimization. Use Cases ๐Ÿ“š CNTK is incredibly versatile and can be used in various ways: ๐Ÿ”ญ As a library in Python, C#, or C++ programs. ๐Ÿ’ก As a standalone machine-learning tool through BrainScript. โ˜• Also accessible from Java programs for CNTK model evaluation. Pricing and Compatibility ๐Ÿ’ฐ๐Ÿ’ป Great news! The 1-bit Stochastic Gradient Descent (1-bit SGD) in CNTK is available without any separate license requirement. It's conveniently provided under the license on GitHub. CNTK supports both 64-bit Linux and 64-bit Windows operating systems. You can easily install it by choosing pre-compiled binary packages or compile the toolkit from the source available on GitHub. ONNX Support ๐ŸŒ Looking for framework interoperability? CNTK has you covered. It's one of the first deep-learning toolkits to support the ONNX format, with the latest release supporting ONNX v1.0. Explore the power of Microsoft Cognitive Toolkit (CNTK) and unlock the potential of deep learning.

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Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit designed for commercial-grade distributed deep learning. However, it is important to note that CNTK is no longer actively developed. The final major release was CNTK 2.7. Features CNTK describes neural networks as a series of computational steps via a directed graph. It allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its model description language (BrainScript). It supports 64-bit Linux or 64-bit Windows operating systems. It is one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format, an open-source shared model representation for framework interoperability and shared optimization. Use Cases CNTK can be used as a library in Python, C#, or C++ programs. It can also be used as a standalone machine-learning tool through its model description language, BrainScript. In addition, the CNTK model evaluation functionality can be used from Java programs. Pricing There is no separate license required to use the 1-bit Stochastic Gradient Descent (1-bit SGD) in CNTK; the 1-bit SGD is available under the license provided in GitHub. Compatibility CNTK supports 64-bit Linux or 64-bit Windows operating systems. It can be installed by choosing pre-compiled binary packages or by compiling the toolkit from the source provided in GitHub. ONNX Support CNTK is one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format. The latest release of CNTK supports ONNX v1.0.

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