Convolution Operation: The Foundation of CNNs
Interactive guide to convolution in CNNs: visualize sliding windows, kernels, stride, padding, and feature detection with step-by-step demos.
10 min readConcept
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architectures
Explore machine learning concepts related to architectures. Clear explanations and practical insights.
12
Concepts Found
Concepts Related to architectures
Dilated Convolutions: Expanding Receptive Fields Efficiently
Understand dilated (atrous) convolutions: how dilation rates expand receptive fields exponentially without extra parameters and how to avoid gridding artifacts.
10 min readConcept
Feature Pyramid Networks
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
6 min readConcept
Receptive Field
Understand receptive fields in CNNs — how convolutional layers expand their field of view, the gap between theoretical and effective receptive fields, and strategies for controlling RF growth.
6 min readConcept
VAE Latent Space: Understanding Variational Autoencoders
Explore VAE latent space in deep learning. Learn variational autoencoder encoding, decoding, interpolation, and the reparameterization trick.
6 min readConcept
CLS Token in Vision Transformers
Learn how the CLS token acts as a global information aggregator in Vision Transformers, enabling whole-image classification through attention mechanisms.
8 min readConcept
Hierarchical Attention in Vision Transformers
Explore how hierarchical attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
6 min readConcept
Multi-Head Attention in Vision Transformers
Explore how multi-head attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
6 min readConcept
Positional Embeddings in Vision Transformers
Explore how positional embeddings enable Vision Transformers (ViT) to process sequential data by encoding relative positions.
5 min readConcept
Interactive Look: Self-Attention in Vision Transformers
Explore how self-attention enables Vision Transformers (ViT) to understand images by capturing global context, with CNN comparison.
6 min readConcept
Adaptive Tiling: Efficient Visual Token Generation
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts by up to 80% while preserving detail where it matters.
7 min readConcept
Skip Connections
Learn how skip connections and residual learning enable training of very deep neural networks. Understand the ResNet revolution with interactive visualizations.
9 min readConcept