Convolution Operation: The Foundation of CNNs
Master the convolution operation through interactive visualizations of sliding windows, feature detection, and the mathematical mechanics behind convolutional neural networks.
11 min readConcept
Tagged with
deep-learning
Explore machine learning concepts related to deep-learning. Clear explanations and practical insights.
16
Concepts Found
Concepts Related to deep-learning
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.
9 min readConcept
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.
8 min readConcept
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.
7 min readConcept
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.
8 min readConcept
VAE Latent Space: Understanding Variational Autoencoders
Explore the latent space of Variational Autoencoders through interactive visualizations of encoding, decoding, interpolation, and the reparameterization trick.
6 min readConcept
Hierarchical Attention in Vision Transformers
Explore how hierarchical attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
1 min readConcept
Hierarchical Attention in Vision Transformers
Explore how hierarchical attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
1 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
Interactively explore how self-attention allows Vision Transformers (ViT) to understand images by capturing global context. Click, explore, and see how it differs from CNNs.
6 min readConcept
Gradient Flow in Deep Networks
Understanding how gradients propagate through deep neural networks and the vanishing/exploding gradient problems.
8 min readConcept
Layer Normalization
Understanding layer normalization technique that normalizes inputs across features, making it ideal for sequence models and transformers.
5 min readConcept
Internal Covariate Shift
Understanding the distribution shift problem in deep neural networks that batch normalization solves.
7 min readConcept
Batch Normalization
Understanding batch normalization technique that normalizes inputs to accelerate training and improve neural network performance.
5 min readConcept
Skip Connections
Understanding skip connections, residual blocks, and their crucial role in training deep neural networks.
4 min readConcept