Update README.md

README.md

@@ -110,9 +110,9 @@ I find that the key of intelligence and cognition is a very interesting subject
 - [LSTM for Human Activity Recognition (HAR)](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) - Tutorial of mine on using LSTMs on time series for classification.
 - [Deep stacked residual bidirectional LSTMs for HAR](https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs) - Improvements on the previous project.
 - [Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Prediction](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) - Tutorial of mine on how to predict temporal sequences of numbers - that may be multichannel.
-- [Hyperopt for a Keras CNN on CIFAR-100](https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100) - Auto (meta) optimizing a neural net (and its architecture) on the CIFAR-100 dataset.
+- [Hyperopt for a Keras CNN on CIFAR-100](https://github.com/guillaume-chevalier/Hyperopt-Keras-CNN-CIFAR-100) - Auto (meta) optimizing a neural net (and its architecture) on the CIFAR-100 dataset.
 - [ML / DL repositories I starred](https://github.com/guillaume-chevalier?direction=desc&page=1&q=machine+OR+deep+OR+learning+OR+rnn+OR+lstm+OR+cnn&sort=stars&tab=stars&utf8=%E2%9C%93) - GitHub is full of nice code samples & projects.
-- [Smoothly Blend Image Patches](https://github.com/Vooban/Smoothly-Blend-Image-Patches) - Smooth patch merger for [semantic segmentation with a U-Net](https://vooban.com/en/tips-articles-geek-stuff/satellite-image-segmentation-workflow-with-u-net/).
+- [Smoothly Blend Image Patches](https://github.com/guillaume-chevalier/Smoothly-Blend-Image-Patches) - Smooth patch merger for [semantic segmentation with a U-Net](https://vooban.com/en/tips-articles-geek-stuff/satellite-image-segmentation-workflow-with-u-net/).
 - [Self Governing Neural Networks (SGNN): the Projection Layer](https://github.com/guillaume-chevalier/SGNN-Self-Governing-Neural-Networks-Projection-Layer) - With this, you can use words in your deep learning models without training nor loading embeddings.
 - [Neuraxle](https://github.com/Neuraxio/Neuraxle) - Neuraxle is a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications.