A notebook covering the data science lifecycle, statistical data types, EDA, data cleaning, visualization, and prediction fundamentals.

A curated reference of 90+ publicly available CT datasets for medical and industrial imaging research, including download links, dataset sizes, and usage notes.

A comprehensive review of deep learning methods for CT reconstruction from incomplete projection data, covering sparse-view, limited-angle, metal artifact reduction, and ring artifact problems.

A technical overview of deep learning reconstruction (DLR) algorithms for CT imaging, comparing FBP, iterative reconstruction, and DLR approaches with clinical applications.

A comprehensive technical guide to 3D U-Net-based CT reconstruction, covering sinogram-to-image domain transfer, patch-based training strategies, and a full PyTorch implementation.

Questions 61-80 covering applied deep learning: self-driving cars, medical imaging systems, NLP pipelines, recommendation systems, and production ML design.

Questions 41-60 covering adversarial examples, style transfer, attention mechanisms, transformers, transfer learning, and model interpretability.

Questions 21-40 covering loss functions, optimization algorithms (SGD, Adam, RMSprop), regularization techniques, batch normalization, and dropout.

Questions 1-20 covering deep learning fundamentals: neural networks, activation functions, backpropagation, CNNs, and optimization basics.

My undergraduate Final Year Project on sparse-view CT reconstruction using physics-guided deep learning, with a companion study guide on the theoretical foundations.