Blog of Kilian Obermeier
Description: Blog about Deep Learning, Machine Learning, and Programming in general
This is a migration guide for TensorFlow users that already know how neural networks work and what a tensor is. I have been using TensorFlow since 2016, but I switched to PyTorch in 2020. Although the key concepts of both frameworks are pretty similar, especially since TF v2, I wanted to make sure that I use PyTorch's API properly and don't overlook some critical difference. Therefore, I read through the currently listed beginner-level PyTorch tutorials , the 14 notes in the PyTorch documentation (as of ver
This post explains how to use one-dimensional causal and dilated convolutions in autoregressive neural networks such as WaveNet. For implementation details, I will use the notation of the tensorflow.keras.layers package, although the concepts themselves are framework-independent.
Say we have some temporal data, for example recordings of human speech. At a sample rate of 16,000 Hz, one second of recorded speech is a one-dimensional array of 16,000 values, as visualized here . Based on the recordings we have, we can compute a probabilistic model of the value at the next time step given the values at the previous time steps. Having a good model for this would be really helpful as it would allow us to generate speech ourselves.
Blog of Kilian Obermeier