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.
Seven Questions to Ask before Introducing AI into Your Project
Published: 05.11.2021
Beyond correlation coefficients and mean squared error
Published: 09.09.2021
Deep lifelong learning — drawing inspiration from the human brain
Published: 02.09.2021
Nine Tools I Wish I Mastered before My PhD in Machine Learning
Published: 26.08.2021
Perceptual Losses for Deep Image Restoration
Published: 08.05.2021
Scientific computing — lessons learned the hard way
Published: 15.04.2021
Deep Image Quality Assessment
Published: 15.03.2021
Active sampling for pairwise comparisons
Published: 08.02.2021
Hyper-parameter tuning with Bayesian optimization or how I carved boats from wood
Published: 12.01.2021
Convolutional neural networks — the essential summary
Published: 10.10.2020
Dataset fusion for large-scale preference aggregation
Published: 03.10.2019
Sampling distributions with an emphasis on Gibbs sampling, practicals and code
Published: 22.04.2019
Confidence intervals: parametric and non-parametric resampling
Published: 26.11.2018
Unsupervised deep learning for data interpolation
Published: 06.10.2018