Feature Pyramid Networks (FPN) Explained
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
10 min readConcept
Tagged with
Deep Learning
Explore machine learning concepts related to Deep Learning. Clear explanations and practical insights.
49
Concepts Found
Concepts Related to Deep Learning
CLS Token in Vision Transformers
Trace how a learned CLS row joins image patches, gathers evidence through self-attention, and becomes the image-level classification readout.
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Tensor Cores: Mixed Precision & Matrix Acceleration
How NVIDIA Tensor Cores accelerate GEMM: warp-level MMA, precision dials (TF32/FP16/BF16/INT8/FP8), shape alignment cliffs, AMP vs model.half(), and when the speedup actually shows up.
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PyTorch DataLoader Pipeline
PyTorch DataLoader deep dive — Dataset, Sampler, Workers, Collate internals, num_workers throughput profiling, memory analysis, serialization costs, production patterns (LMDB, WebDataset), and bottleneck diagnosis.
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Hierarchical Attention in Vision Transformers
Trace how local windows, shifted cross-window exchange, and patch merging turn one high-resolution token grid into a multi-scale vision hierarchy.
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Multi-Head Attention
How multi-head attention runs scaled dot-product attention in parallel across several representation subspaces to build context-aware token embeddings.
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Anchor-Based vs Anchor-Free Object Detection
Compare anchor-based vs anchor-free object detection: Faster R-CNN and RetinaNet anchors vs FCOS and CenterNet point-based methods.
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Representation Collapse in Self-Supervised Learning
Understanding complete, dimensional, and cluster collapse — the failure modes that every self-supervised method must prevent. Learn why collapse happens and how contrastive, asymmetric, regularization, and masking approaches solve it.
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Positional Embeddings in Vision Transformers
Explore how positional embeddings enable Vision Transformers (ViT) to process sequential data by encoding relative positions.
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Batch Norm vs Layer Norm: When to Use Which
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.
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Self-Attention in Vision Transformers
Follow one image patch through Q/K/V projection, scaled scores, row-wise softmax, value mixing, and the residual update inside a Vision Transformer.
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DETR Explained: Object Detection with Transformers
Understanding end-to-end object detection with transformers, from DETR's object queries to bipartite matching and attention-based localization
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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.
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ALiBi: Attention with Linear Biases
Learn ALiBi, the position encoding method that adds linear biases to attention scores for exceptional length extrapolation in transformers.
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Cross-Entropy Loss for Classification
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
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Flash Attention vs MHA vs GQA vs MQA: Comparing Attention Mechanisms
How Flash Attention, Multi-Head Attention (MHA), Grouped-Query Attention (GQA), and Multi-Query Attention (MQA) compare — algorithm vs architecture, KV-cache memory, quality trade-offs, and how to choose for production transformer inference.
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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.
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Attention Sinks: Stable Streaming LLMs
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
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Receptive Field in CNNs
Understand receptive fields in CNNs: how convolutional layers expand their field of view and the gap between theoretical and effective receptive fields.
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Cross-Attention: Bridging Different Modalities
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.
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VAE Latent Space: Understanding Variational Autoencoders
Explore VAE latent space in deep learning. Learn variational autoencoder encoding, decoding, interpolation, and the reparameterization trick.
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Grouped-Query Attention: Head Sharing and KV Cache
Trace how grouped-query attention keeps independent query heads while sharing fewer key/value heads, projections, and compact KV-cache rows during LLM decoding.
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Contrastive Loss for Representation Learning
Understand contrastive loss for representation learning: interactive demos of InfoNCE, triplet loss, and embedding space clustering with temperature tuning.
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Linear Attention Approximations
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
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Dropout Regularization
Understand dropout regularization: how randomly silencing neurons prevents overfitting, the inverted dropout trick, and when to use each dropout variant.
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Masked and Causal Attention
Trace causal attention from shifted next-token labels through the lower-triangular mask, pre-softmax blocking, parallel training, and incremental decoding.
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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.
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Multi-Query Attention (MQA)
Learn Multi-Query Attention (MQA), the optimization that shares keys and values across attention heads for massive memory savings.
6 min readConcept
He/Kaiming Initialization
Learn He (Kaiming) initialization for ReLU networks: why ReLU needs special weight initialization, variance flow, and dead neurons explained.
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Rotary Position Embeddings (RoPE)
Learn Rotary Position Embeddings (RoPE), the elegant position encoding using rotation matrices, powering LLaMA, Mistral, and modern LLMs.
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KL Divergence in Machine Learning
Learn KL divergence for machine learning: measure distribution differences in VAEs, knowledge distillation, and variational inference.
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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
MSE and MAE Loss Functions
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.
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Sliding Window Attention
Sliding Window Attention for long sequences: local context windows enable O(n) complexity, used in Mistral and Longformer models.
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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.
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Sparse Attention Patterns
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
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Adaptive Tiling: Efficient Visual Token Generation
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts while preserving detail.
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Emergent Abilities in Large Language Models
Explore emergent abilities in large language models: sudden capabilities at scale thresholds, phase transitions, and the mirage debate.
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Prompt Engineering for LLMs
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques.
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Prompt Influence Flow Through Transformer Layers
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
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NVIDIA vs AMD for Deep Learning: CUDA vs ROCm and the Datacenter Accelerators
NVIDIA vs AMD for deep learning compared at both layers: the CUDA vs ROCm software moat, the microarchitecture (warp vs wavefront, SM vs CU, Tensor vs Matrix Cores), and the datacenter accelerators (H100/H200/B200 vs MI300X/MI325X).
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Neural Scaling Laws Explained
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance.
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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.
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Gradient Flow in Deep Networks
Learn how gradients propagate through deep neural networks during backpropagation. Understand vanishing and exploding gradient problems.
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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.
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Layer Normalization for Transformers
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training.
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Internal Covariate Shift
Understand internal covariate shift: why layer input distributions change during training, how it slows convergence, and how batch norm fixes it.
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Batch Normalization in Deep Learning
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization.
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Skip Connections in Neural Networks
Learn how skip connections and residual learning enable training of very deep neural networks. Understand the ResNet revolution with interactive visualizations.
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