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_posts/2024-09-01-blog-4.md

@@ -7,7 +7,7 @@ tags:
   - Neural Style Transfer
   - Deep Learning
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-[**_Neural style transfer (NST)_**](https://en.wikipedia.org/wiki/Neural_style_transfer) serves as an essential starting point those interested in deep learning, incorporating critical components or techniques such as [**_convolutional neural networks (CNNs)_**](https://en.wikipedia.org/wiki/Convolutional_neural_network), VGG network, [**_residual networks_**](https://en.wikipedia.org/wiki/Residual_neural_network), upsampling and normalization. The basic neural style transfer method captures and manipulates image features using CNNs and the VGG network directly over the input image. In contrast, the fast neural style transfer method employs a dataset to train an inference neural network that can be used for real-time style transfer. This blogs provides the explanation of the implementation of both methods. Corresponding repository can be found [here (Github)](https://github.com/lihanlian/basic-and-fast-neural-style-transfer). 
+[**_Neural style transfer (NST)_**](https://en.wikipedia.org/wiki/Neural_style_transfer) serves as an essential starting point those interested in deep learning, incorporating critical components or techniques such as [**_convolutional neural networks (CNNs)_**](https://en.wikipedia.org/wiki/Convolutional_neural_network), VGG network, [**_residual networks_**](https://en.wikipedia.org/wiki/Residual_neural_network), upsampling and normalization. The basic neural style transfer method captures and manipulates image features using CNNs and the VGG network directly over the input image. In contrast, the fast neural style transfer method employs a dataset to train an inference neural network that can be used for real-time style transfer. This blogs provides the explanation of the implementation of both methods. Corresponding repository can be found <i class="fa-brands fa-github"></i> [here](https://github.com/lihanlian/basic-and-fast-neural-style-transfer). 
 
 ## Basic NST