update links

Author: HurleyWong

README.md

@@ -12,9 +12,9 @@
 * [Intro to Pytorch](Intro_to_PyTorch.ipynb): The purpose of this notebook is to give you a general understanding of how to use the [PyTorch](https://pytorch.org/) Python package for writing, training and analysing neural networks.
 * [Linear Classifier](Linear_Classifier.ipynb): Train a multiclass linear classifier on the CIFAR10 dataset in [Keras](https://keras.io/).
 * [Using Word2Vec Embeddings](Using_Word2Vec_Embeddings.ipynb): News Category Classification using Word2Vec embeddings with two Python libraries, [scikit-learn](https://scikit-learn.org/stable/) and [gensim](https://radimrehurek.com/gensim/).
-* [Image Classification with CNN](Image_Classification_with_Convolutional_Neural_Networks_ImageNet10.ipynb): Use convolutional neural networks, from development to training and testing. Plot feature maps and filters during the training process and testing. Explore methods of improving performance on a network. The details can be found in this article [Image Classification with Convolutional Neural Networks - ImageNet10](https://article.withh.life/2020/03/image-classification-cnn/).
-* [Image Caption Generation](Image_Caption_Generation.ipynb): Do text pre-processing and text embeddings with an image to text model, compare the performance and usage of RNNs versus LSTMs as sequence generators. The details can be found in this article [Image Caption Generation with BLEU](https://article.withh.life/2020/04/image-caption-generation/)
-* [Perceptron](Perceptron): Compare a simple perceptron and a simple network built by myself to a network model by Keras. The details can be found in this article [Perceptron Algorithm and Backpropagation](https://article.withh.life/2020/04/iris-perceptron-backpropagation/).
+* [Image Classification with CNN](Image_Classification_with_Convolutional_Neural_Networks_ImageNet10.ipynb): Use convolutional neural networks, from development to training and testing. Plot feature maps and filters during the training process and testing. Explore methods of improving performance on a network. The details can be found in this article [Image Classification with Convolutional Neural Networks - ImageNet10](https://blog.withh.life/2020/03/image-classification-cnn/).
+* [Image Caption Generation](Image_Caption_Generation.ipynb): Do text pre-processing and text embeddings with an image to text model, compare the performance and usage of RNNs versus LSTMs as sequence generators. The details can be found in this article [Image Caption Generation with BLEU](https://blog.withh.life/2020/04/image-caption-generation/)
+* [Perceptron](Perceptron): Compare a simple perceptron and a simple network built by myself to a network model by Keras. The details can be found in this article [Perceptron Algorithm and Backpropagation](https://blog.withh.life/2020/04/iris-perceptron-backpropagation/).
 
 ### Training and test dataset
 * [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html): The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.