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doc: extended set of supported applications and functionality
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README.md

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@@ -21,13 +21,14 @@ of the following deep learning topologies and variations of these.
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| Application | Example topology
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|:--- |:---
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| Image recognition | AlexNet, VGG, GoogleNet, ResNet
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| Image segmenation | FCN, SegNet
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| Image recognition | AlexNet, VGG, GoogleNet, ResNet, MobileNet
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| Image segmenation | FCN, SegNet, MaskRCNN, U-Net
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| Volumetric segmentation | 3D-Unet
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| Object detection | SSD, Faster R-CNN, Yolo
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| Neural Machine Translation (experimental) | GNMT
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| Speech Recognition (experimental) | DeepSpeech
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| Adversarial Networks | DCGAN, 3DGAN
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| Reinforcement Learning | A3C
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Intel MKL-DNN is used in the following software products:
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* [Caffe\* Optimized for Intel Architecture](https://github.com/intel/caffe)

doc/mainpage.md

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@@ -19,7 +19,7 @@ The table below summarizes the list of supported functions and their variants.
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| Primitive class | Primitive | fp32 training | fp32 inference | int8 inference |
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| :---------------- | :----------------------- | :-----------: | :------------: | :------------: |
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| Convolution | 2D direct convolution | x | x | x |
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| | 2D direct deconvolution | x | x | |
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| | 2D direct deconvolution | x | x | x |
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| | 2D winograd convolution | x | x | x |
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| | 3D direct convolution | x | x | |
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| | 3D direct deconvolution | x | x | |
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| | Bounded ReLU | | x | |
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| | Soft ReLU | | x | |
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| | Logistic regression | | x | |
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| | Softmax | | x | |
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| | Softmax | x | x | |
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| Data manipulation | Reorder/quantization | x | x | x |
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| | Sum | x | x | x |
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| | Concat | x | x | x |

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