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Bivariate Normal Distribution

Bivariate normal distribution – derivation by linear transformation of a random vector for two independent Gaussians

In an another post on properties of a Bivariate Normal Distribution [BVD] I have motivated the form of its probability density function [pdf] by symmetry arguments and the underlying probability density functions of its marginals, namely 1-dimensional Gaussians. In this post we will derive the probability density function by following the line of argumentation for a general Multivariate Normal Distribution… Read More »Bivariate normal distribution – derivation by linear transformation of a random vector for two independent Gaussians

Performance comparison of PyTorch and Keras3 with TF2 and Torch backends for a small NN-model on a Nvidia 4060 TI – II – Keras with Torch backend

Not all tasks in Machine Learning [ML] require big LLMs or LLM-based interfaces. Actually, many interesting ML-tasks can be solved with neural networks [NNs] that fit well into the VRAM of a modern GPU or TPU affordable even for private persons. This statements holds in particular for image processing. We have groups of people either learning to work with ML… Read More »Performance comparison of PyTorch and Keras3 with TF2 and Torch backends for a small NN-model on a Nvidia 4060 TI – II – Keras with Torch backend

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

PyTorch / datasets / dataloader / data transfer to GPU – III – prepared tensor datasets and preloading to GPU

In this post series we have a look at PyTorch dataloaders and Torchvision image datasets (downloaded via PyTorch modules). PyTorch DataLoaders retrieve batches of dataset elements and transfer them to neural networks [NN] on a computation device – e.g. a CUDA driven graphics card. A central dataset functions applies defined transformation operations to its elements. We analyze the impact of… Read More »PyTorch / datasets / dataloader / data transfer to GPU – III – prepared tensor datasets and preloading to GPU