Cross-Entropy Loss: The Foundation of Classification
Understand cross-entropy loss through interactive visualizations of probability distributions, gradient flow, and its connection to maximum likelihood estimation.
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ML Concepts - Page 9
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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Concepts - Page 9
Dilated Convolutions: Expanding Receptive Fields Efficiently
Master dilated (atrous) convolutions through interactive visualizations of dilation rates, receptive field expansion, gridding artifacts, and applications in segmentation.
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Feature Pyramid Networks: Multi-Scale Feature Fusion
Understand Feature Pyramid Networks (FPN) through interactive visualizations of top-down pathways, lateral connections, and multi-scale object detection.
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Receptive Field: Understanding CNN Vision
Explore how receptive fields grow through CNN layers with interactive visualizations of effective vs theoretical fields, architecture comparisons, and pixel contributions.
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VAE Latent Space: Understanding Variational Autoencoders
Explore the latent space of Variational Autoencoders through interactive visualizations of encoding, decoding, interpolation, and the reparameterization trick.
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Virtual Memory & Paging: Abstracting Physical Memory
Explore virtual memory management through interactive visualizations of page tables, TLB operations, page faults, and memory mapping.
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Showing 49-54 of 136 articles