<p>A lot of it depends on what you're doing. Generally, you'll want to try an autoencoder or a transformer and do unsupervised/semisupervised learning.</p>
<p>Here's some material that might give some insight into some methods.<br />
<a href="https://arxiv.org/abs/2006.07733" rel="nofollow noreferrer">https://arxiv.org/abs/2006.07733</a> (Bootstrap your own latent -- Deepmind)<br />
<a href="https://arxiv.org/abs/1610.02242" rel="nofollow noreferrer">https://arxiv.org/abs/1610.02242</a> (Temporal ensembling for semisupervised learning -- Timo Aina)<br />
<a href="https://www.coursera.org/lecture/intro-to-deep-learning/autoencoders-101-QqBOa" rel="nofollow noreferrer">https://www.coursera.org/lecture/intro-to-deep-learning/autoencoders-101-QqBOa</a>  (Autoencoder -- Andrew Ng)</p>
Feature extraction using deep learning for more than one class
<p>I have a time series dataset and I want to extract its features using BRNN or CNN -RNN ( python programming language)</p>
<p>First, I train the model with the classification layer and obtain best accuracy.</p>
<p>Then, I want to take the features from one of model's hidden layer.</p>
<p>But I have 41 class how can I extract the features of all these classes and how I know the extracted features belong to which class?</p>
<p>Because I want each class with its extracted features to make some calculation later.</p>