Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But do you know what it has actually learned? A team of University of Cambridge researchers propose ‘SymTorch’, a library designed to integrate symbolic regression (SR) into…
In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, einsum, and pack/unpack let us reshape, aggregate, and combine tensors without relying on error-prone manual dimension handling. We focus on real deep-learning patterns, such as vision…
In this tutorial, we demonstrate a realistic data poisoning attack by manipulating labels in the CIFAR-10 dataset and observing its impact on model behavior. We construct a clean and a poisoned training pipeline side by side, using a ResNet-style convolutional network to ensure stable, comparable learning dynamics. By selectively flipping a fraction of samples from…
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The selection between PyTorch and TensorFlow stays probably the most debated selections in AI improvement. Each frameworks have developed dramatically since their inception, converging in some areas whereas sustaining distinct strengths. This text explores the most recent patterns from the great survey paper from Alfaisal College, Saudi Arabia, synthesizing usability, efficiency, deployment, and ecosystem concerns…
This article draws insights from the paper ‘Mixture of Experts in Large Language Models’ (arXiv:2507.11181), examining how MoE… Continue reading on Artificial Intelligence in Plain English »