#deep learning

Notes on the Transformer architecture: attention mechanisms, multi-head attention, positional encoding, and encoder/decoder layers with PyTorch implementations.

A compilation of interesting papers I enjoyed reading, including I-Con, Denoising Diffusion Probabilistic Models, and SpiRobs.

Notes on the mathematical foundations of diffusion models, including the noising process, denoising process, loss function, and training/generation loops.

A talk covering miniaturization techniques for deep learning models operating in real-time hearing aid environments.

A study of Acoustic Scene Classification (ASC) for hearing aids, leading to the development of an industrial ASC model based on a convolutional neural network designed to run in hearing aids for noise suppression purposes.

Implementation of the Denoising Diffusion Models paper on CIFAR-10, using a pretrained U-Net and sinusoidal time embeddings.

A lightweight and flexible early stopping utility for PyTorch training loops, inspired by the Keras/TensorFlow EarlyStopping callback.

Implementation of transformer encoder-decoder model based on the Attention is all you need paper.

An introductory course on neural networks applied to digital signal processing and sound synthesis.