Category: visualization


  • Visualization of Learning Representations with PCA and UMAP

    Deep learning models, such as neural networks, build abstract representations of data during the training phase. In this stage, the goal is to optimize a cost function—either by minimizing or maximizing it—using algorithms like those from the gradient descent family. In neural networks, this process is applied to each layer of the model. Thus, each…