Code AUTOENCODERS w/ Python + KERAS Layers (Colab, TensorFlow2, Autumn 2022) скачать в хорошем качестве

Code AUTOENCODERS w/ Python + KERAS Layers (Colab, TensorFlow2, Autumn 2022)

An elegant way to code AUTOENCODERS with KERAS layers in TensorFlow2 on COLAB w/ Python. Autoencoders are applied for dimensionality reduction, where PCA fails for non-linearity. A coding Example on COLAB shows the way forward to code your own AUTOENCODER for your low-dimensional latent space encoding (low-dim embedding). Note: A KERAS layer is a mathematical abstraction, based on TF2, but AUTOENCODERS are simply feed-forward neural networks. Autoencoders do not rely on self-attention, like encoders from within transformers. Autoencoders encode information from a high-dimensional input space to a low-dimensional latent space, which includes all relevant information from the input space. official links and COLAB NB #colab : https://colab.research.google.com/git... TF2 tutorial (recommended): Intro to AUTOENCODER: https://www.tensorflow.org/tutorials/... #deeplearning #ai #encoder #autoencoder #stablediffusion