Explore machine learning concepts related to architectures. Clear explanations and practical insights.
BatchNorm normalizes over the batch and spatial axes; LayerNorm normalizes over the channel and spatial axes for each sample. The choice changes whether your model trains stably with batch=1, depends on batch composition at inference, and behaves consistently across train and eval.
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
Interactive guide to convolution in CNNs: visualize sliding windows, kernels, stride, padding, and feature detection with step-by-step demos.
Understand dilated (atrous) convolutions: how dilation rates expand receptive fields exponentially without extra parameters and how to avoid gridding artifacts.
Understand receptive fields in CNNs: how convolutional layers expand their field of view and the gap between theoretical and effective receptive fields.
Explore VAE latent space in deep learning. Learn variational autoencoder encoding, decoding, interpolation, and the reparameterization trick.
Learn how the CLS token acts as a global information aggregator in Vision Transformers, enabling whole-image classification through attention mechanisms.
Explore how hierarchical attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
Explore how multi-head attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
Explore how positional embeddings enable Vision Transformers (ViT) to process sequential data by encoding relative positions.
Explore how self-attention enables Vision Transformers (ViT) to understand images by capturing global context, with CNN comparison.
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts while preserving detail.
Learn how skip connections and residual learning enable training of very deep neural networks. Understand the ResNet revolution with interactive visualizations.