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Machine Learning

Performance of PyTorch vs. Keras 3 with tensorflow/torch backends for a small NN-model on a Nvidia 4060 TI – I – Torch vs. Keras3/TF2 and relevant parameters

Today’s world of Machine Learning is characterized by competing frameworks. I am used to the combination of Keras with the Tensorflow2 [TF2] backend, but have turned now to using PyTorch in addition. As a beginner with PyTorch, I wanted to get an impression about potential performance advantages in comparison with the Keras/TF2 framework combination. I had read about significant performance… Read More »Performance of PyTorch vs. Keras 3 with tensorflow/torch backends for a small NN-model on a Nvidia 4060 TI – I – Torch vs. Keras3/TF2 and relevant parameters

ResNet56V2 Convergence within epoch 20

AdamW for a ResNet56v2 – VI – Super-Convergence after improving the ResNetV2

In previous posts of this series I have shown that a Resnet56V2 with AdamW can converge to acceptable values of the validation accuracy for the CIFAR10 dataset – within less than 26 epochs. An optimal schedule of the learning rate [LR] and optimal values for the weight decay parameter [WD] were required. My network – a variation of the ResNetV2-structure… Read More »AdamW for a ResNet56v2 – VI – Super-Convergence after improving the ResNetV2

CAE generated face on background of a MND

Latent space distribution of a CAE for face images – I – unenforced Multivariate Normal Distributions

The analysis of face images by a trained Autoencoder and the generation of face images from statistical vectors is a classical task in Machine Learning. In this post series I want to clarify the properties of vector distributions for face images generated by a trained standard Convolutional Autoencoder [CAE] in its latent space. The dataset primarily used is the CelebA… Read More »Latent space distribution of a CAE for face images – I – unenforced Multivariate Normal Distributions

Concentric surfaces of ellipsoids

Multivariate Normal Distributions – III – Variance-Covariance Matrix and a distance measure for vectors of non-degenerate distributions

In previous posts of this series I have motivated the functional form of the probability density of a so called “non-degenerate Multivariate Normal Distribution“. In this post we will have a closer look at the matrix Σ that controls the probability density function [pdf] of such a distribution. We will show that it actually is the covariance matrix of the… Read More »Multivariate Normal Distributions – III – Variance-Covariance Matrix and a distance measure for vectors of non-degenerate distributions