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
Tagged with
deep-learning
Explore machine learning concepts related to deep-learning. Clear explanations and practical insights.
43
Concepts Found
Concepts Related to deep-learning
Cross-Entropy Loss
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
9 min readConcept
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
MHA vs GQA vs MQA: Choosing the Right Attention
Compare Multi-Head, Grouped-Query, and Multi-Query Attention mechanisms to understand their trade-offs and choose the optimal approach for your use case.
9 min readConcept
Attention Sinks: Stable Streaming LLMs
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
17 min readConcept
ALiBi: Attention with Linear Biases
Learn ALiBi, the position encoding method that adds linear biases to attention scores for exceptional length extrapolation in transformers.
19 min readConcept
Cross-Attention: Bridging Different Modalities
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.
15 min readConcept
Grouped-Query Attention (GQA)
Learn how Grouped-Query Attention (GQA) balances Multi-Head quality with Multi-Query efficiency for faster LLM inference.
7 min readConcept
Linear Attention Approximations
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
6 min readConcept
Masked and Causal Attention
Learn how masked attention enables autoregressive generation and prevents information leakage in transformers and language models.
7 min readConcept
Multi-Query Attention (MQA)
Learn Multi-Query Attention (MQA), the optimization that shares keys and values across attention heads for massive memory savings.
7 min readConcept
Rotary Position Embeddings (RoPE)
Learn Rotary Position Embeddings (RoPE), the elegant position encoding using rotation matrices, powering LLaMA, Mistral, and modern LLMs.
8 min readConcept
Scaled Dot-Product Attention
Master scaled dot-product attention, the fundamental transformer building block. Learn why scaling is crucial for stable training.
6 min readConcept
Sliding Window Attention
Sliding Window Attention for long sequences: local context windows enable O(n) complexity, used in Mistral and Longformer models.
14 min readConcept
Sparse Attention Patterns
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
7 min readConcept
Contrastive Loss
Understand contrastive loss for representation learning: interactive demos of InfoNCE, triplet loss, and embedding space clustering with temperature tuning.
8 min readConcept
Dropout Regularization
Understand dropout regularization: how randomly silencing neurons prevents overfitting, the inverted dropout trick, and when to use each dropout variant.
10 min readConcept
Focal Loss: Focusing on Hard Examples
Learn focal loss for deep learning: down-weight easy examples, focus on hard ones. Interactive demos of gamma, alpha balancing, and RetinaNet.
9 min readConcept
He/Kaiming Initialization
Learn He (Kaiming) initialization for ReLU neural networks: understand why ReLU needs special weight initialization, visualize variance flow, and see dead neurons in action.
7 min readConcept
KL Divergence
Learn KL divergence for machine learning: measure distribution differences in VAEs, knowledge distillation, and variational inference with interactive visualizations.
7 min readConcept
MSE and MAE Loss Functions
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.
8 min readConcept
Xavier/Glorot Initialization
Learn Xavier (Glorot) initialization: how it balances forward signals and backward gradients to enable stable deep network training with tanh and sigmoid.
8 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
Emergent Abilities in Large Language Models
Explore emergent abilities in large language models: sudden capabilities that appear at scale thresholds, phase transitions, and the mirage debate, with interactive visualizations.
7 min readConcept
Prompt Engineering
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques with interactive visualizations.
6 min readConcept
Prompt Influence Flow: How Instructions Propagate Through Model Layers
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
6 min readConcept
Neural Scaling Laws
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance, with interactive visualizations.
8 min readConcept
Visual Complexity Analysis: Smart Image Processing
Learn visual complexity analysis in deep learning - how neural networks measure entropy, edges, and saliency for adaptive image processing.
8 min readConcept
Gradient Flow in Deep Networks
Learn how gradients propagate through deep neural networks during backpropagation. Understand vanishing and exploding gradient problems with interactive visualizations.
8 min readConcept
PyTorch DataLoader Pipeline
Understanding how PyTorch DataLoader moves data from disk through CPU to GPU, including Dataset, Sampler, Workers, and Collate components.
4 min readConcept
NAdam: Nesterov-Accelerated Adam
Understand the NAdam optimizer that fuses Adam adaptive learning rates with Nesterov look-ahead momentum for faster, smoother convergence in deep learning.
6 min readConcept
Layer Normalization
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training with interactive visualizations.
7 min readConcept
Internal Covariate Shift
Understand internal covariate shift in deep learning: why layer input distributions change during training, how it slows convergence, and how batch normalization fixes it.
8 min readConcept
Batch Normalization
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization with interactive visualizations.
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