Explore machine learning concepts related to deep-learning. Clear explanations and practical insights.
PyTorch DataLoader deep dive — Dataset, Sampler, Workers, Collate internals, num_workers throughput profiling, memory analysis, serialization costs, production patterns (LMDB, WebDataset), and bottleneck diagnosis.
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.
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.
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
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.
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
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 ALiBi, the position encoding method that adds linear biases to attention scores for exceptional length extrapolation in transformers.
Compare Multi-Head, Grouped-Query, and Multi-Query Attention mechanisms to understand their trade-offs and choose the optimal approach for your use case.
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.
Learn how Grouped-Query Attention (GQA) balances Multi-Head quality with Multi-Query efficiency for faster LLM inference.
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
Learn how masked attention enables autoregressive generation and prevents information leakage in transformers and language models.
Learn Multi-Query Attention (MQA), the optimization that shares keys and values across attention heads for massive memory savings.
Learn Rotary Position Embeddings (RoPE), the elegant position encoding using rotation matrices, powering LLaMA, Mistral, and modern LLMs.
Master scaled dot-product attention, the fundamental transformer building block. Learn why scaling is crucial for stable training.
Sliding Window Attention for long sequences: local context windows enable O(n) complexity, used in Mistral and Longformer models.
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
Understand contrastive loss for representation learning: interactive demos of InfoNCE, triplet loss, and embedding space clustering with temperature tuning.
Understand dropout regularization: how randomly silencing neurons prevents overfitting, the inverted dropout trick, and when to use each dropout variant.
Learn focal loss for deep learning: down-weight easy examples, focus on hard ones. Interactive demos of gamma, alpha balancing, and RetinaNet.
Learn He (Kaiming) initialization for ReLU networks: why ReLU needs special weight initialization, variance flow, and dead neurons explained.
Learn KL divergence for machine learning: measure distribution differences in VAEs, knowledge distillation, and variational inference.
Learn Xavier (Glorot) initialization: how it balances forward signals and backward gradients to enable stable deep network training with tanh and sigmoid.
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts while preserving detail.
Explore emergent abilities in large language models: sudden capabilities at scale thresholds, phase transitions, and the mirage debate.
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques.
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance.
Learn visual complexity analysis in deep learning - how neural networks measure entropy, edges, and saliency for adaptive image processing.
Learn how gradients propagate through deep neural networks during backpropagation. Understand vanishing and exploding gradient problems.
Understand the NAdam optimizer that fuses Adam adaptive learning rates with Nesterov look-ahead momentum for faster, smoother convergence in deep learning.
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training.
Understand internal covariate shift: why layer input distributions change during training, how it slows convergence, and how batch norm fixes it.
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization.
Learn how skip connections and residual learning enable training of very deep neural networks. Understand the ResNet revolution with interactive visualizations.