The Microsoft Cognitive Toolkit
A framework for building deep learning models using computational graphs
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+ | Scalability | Scales seamlessly from CPUs to GPUs to clusters, handling massive datasets with ease. |
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+ | Python/C++ API Support | Includes Python/C++ API support offering flexibility and developer choice |
+ | Tutorials | Onboard quickly with Python notebooks and examples. |
+ | Layers Interface | An easy-to-use interface for building neural networks. |
+ | Custom Layer Support | Allows users to define their custom layers for specific tasks, extending its functionality |
+ | Sequence-to-Sequence with Attention | Supports sequence-to-sequence models with attention mechanisms |
+ | Batch Normalization | Built-in batch normalization for improved training stability. |
+ | Convolutional Neural Networks (CNN) | Powerful for image recognition and classification. |
+ | Feedforward Neural Networks (FFN) | Efficiently processes dense data. |
+ | Recurrent Neural Networks (RNN) | Handles sequential data like speech and text. |
+ | BrainScript Support | Handles multi-dimensional sparse and dense data. |
+ | Automatic Differentiation | Automatically computes gradients, a critical step in training neural networks |
+ | Built-in Data Readers | Built-in data readers optimized for various formats’ data loading and parsing |
System Requirements
Version ↓
# | Recommended |
---|---|
1 | Ubuntu 16.04 LTS (64 bit) |
2 | GNU C++ 5.4.0 |
3 | Open MPI v. 1.10.7 |
4 | Intel® MKLML library |
5 |
|
6 | OpenCV v.3.1.0 |
7 | zlib v.1.2.8 |
8 | libzip v.1.1.2 |
9 | OpenJDK 7, 64-bit |
10 | Anaconda3 4.1.1 (64 bit) |
11 | The presented set of product versions is not restrictive, i.e. CNTK may work well in many other configurations |
# | Recommended |
---|---|
1 |
|
2 | Visual Studio Enterprise 2017 |
3 | Microsoft MPI v. 7.0 |
4 | Intel® MKLML library |
5 |
|
6 | OpenCV v.3.1.0 |
7 | zlib v.1.2.8 |
8 | libzip v.1.1.3 |
9 | Java SE Development Kit 8 v1.8.0_131, 64-bit |
10 | Anaconda3 4.1.1 (64 bit) |
11 | The presented set of product versions is not restrictive, i.e. CNTK may work well in many other configurations |
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