I regularly write about topics in Machine Learning and Artificial Intelligence. Some of these posts are my personal reference notes, which, I hope would also be useful to others. Whenever applicable I also provide code in the form of jupyter notebooks. To receive regular updates follow me on Medium.

Perceptual Losses for Deep Image Restoration

Scientific computing — lessons learned the hard way

Deep Image Quality Assessment

Active sampling for pairwise comparisons

Hyper-parameter tuning with Bayesian optimization or how I carved boats from wood

Convolutional neural networks — the essential summary (Part 2)

Convolutional neural networks — the essential summary (Part 1)

Dataset fusion for large-scale preference aggregation

Sampling distributions with an emphasis on Gibbs sampling, practicals and code

Confidence intervals: parametric and non-parametric resampling

Unsupervised deep learning for data interpolation