How to cluster text-based software requirements I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github code of unsupervised neural network that groups texts based on themes and words similarities?

Thank you very much for your answers!
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93