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@@ -214,7 +214,7 @@
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18. [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)
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19. [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning)
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20. [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search)
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-21. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
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+21. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://medium.com/sicara/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
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22. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)
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23. [Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras](https://ahmedbesbes.com/understanding-deep-convolutional-neural-networks-with-a-practical-use-case-in-tensorflow-and-keras.html)
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24. [Overview and benchmark of traditional and deep learning models in text classification](https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html)
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@@ -357,7 +357,7 @@
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16. [nrl.navy.mil/itd/aic](http://www.nrl.navy.mil/itd/aic/)
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17. [hips.seas.harvard.edu](http://hips.seas.harvard.edu/)
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18. [AI Weekly](http://aiweekly.co)
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-19. [stat.ucla.edu](http://www.stat.ucla.edu/~junhua.mao/m-RNN.html)
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+19. [stat.ucla.edu](http://statistics.ucla.edu/)
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20. [deeplearning.cs.toronto.edu](http://deeplearning.cs.toronto.edu/i2t)
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21. [jeffdonahue.com/lrcn/](http://jeffdonahue.com/lrcn/)
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22. [visualqa.org](http://www.visualqa.org/)
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Chaitanya Prakash Bapat