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.
41
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
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
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
ALiBi: Attention with Linear Biases
Understand ALiBi, the position encoding method that adds linear biases to attention scores, enabling exceptional length extrapolation without position embeddings.
19 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
Understand attention sinks, the phenomenon where LLMs concentrate attention on initial tokens, and how preserving them enables infinite-length streaming inference.
17 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 balances the quality of Multi-Head Attention with the efficiency of Multi-Query Attention, enabling faster inference in large language models.
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, essential for language models and sequential generation.
7 min readConcept
Multi-Query Attention (MQA)
Understand Multi-Query Attention, the radical efficiency optimization that shares keys and values across all attention heads, enabling massive memory savings for inference.
7 min readConcept
Rotary Position Embeddings (RoPE)
Understand Rotary Position Embeddings, the elegant position encoding method that encodes relative positions through rotation matrices, used in LLaMA, GPT-NeoX, and most modern LLMs.
8 min readConcept
Scaled Dot-Product Attention
Master the fundamental building block of transformers - scaled dot-product attention. Learn why scaling is crucial and how the mechanism enables parallel computation.
6 min readConcept
Sliding Window Attention
Learn how Sliding Window Attention enables efficient processing of long sequences by limiting attention to local context windows, used in Mistral and Longformer.
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
He/Kaiming Initialization
Master He (Kaiming) initialization, the optimal weight initialization technique for ReLU networks that prevents gradient vanishing in deep neural architectures.
9 min readConcept
Xavier/Glorot Initialization
Understand Xavier (Glorot) initialization, the weight initialization technique that maintains signal variance across layers for stable deep network training.
7 min readConcept
Contrastive Loss Functions
Master contrastive loss functions including InfoNCE, NT-Xent, and Triplet Loss for representation learning and self-supervised training.
11 min readConcept
Focal Loss for Imbalanced Classification
Master focal loss, the game-changing loss function that addresses extreme class imbalance by down-weighting easy examples and focusing on hard negatives.
10 min readConcept
KL Divergence
Understand Kullback-Leibler divergence, the fundamental measure of difference between probability distributions used in VAEs, information theory, and model compression.
10 min readConcept
MSE and MAE Loss Functions
Understand Mean Squared Error (MSE) and Mean Absolute Error (MAE), the fundamental loss functions for regression tasks with different sensitivity to outliers.
8 min readConcept
Dropout Regularization
Master dropout, the powerful regularization technique that prevents overfitting by randomly deactivating neurons during training, creating an ensemble of sub-networks.
11 min readConcept
Adaptive Tiling: Efficient Visual Token Generation
Understanding adaptive tiling in vision transformers - a technique that dynamically adjusts image partitioning based on complexity to optimize token usage while preserving detail.
7 min readConcept
Emergent Abilities: When AI Suddenly "Gets It"
Understanding emergent abilities in large language models - sudden capabilities that appear at scale thresholds, from arithmetic to reasoning and self-reflection.
8 min readConcept
Prompt Engineering: Guiding AI Through Language
Master the art of prompt engineering - from basic composition to advanced techniques like Chain-of-Thought and Tree-of-Thoughts.
4 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: The Mathematics of Model Performance
Understanding neural scaling laws - the power law relationships between model size, data, compute, and performance that govern AI capabilities and guide development decisions.
8 min readConcept
Visual Complexity Analysis: Smart Image Processing
Understanding how AI models analyze visual complexity to optimize processing - measuring entropy, edge density, saliency, and texture for intelligent resource allocation.
8 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