Neural network fundamentals: normalization, convolutions, losses, graph networks, and training dynamics.
How the Calinski-Harabasz index evaluates clustering quality by measuring the ratio of between-cluster to within-cluster variance — fast, intuitive, and ideal for k-selection with convex clusters.
How the silhouette score measures clustering quality for every individual point — comparing intra-cluster cohesion to nearest-cluster separation, with per-point diagnostics that work for arbitrary cluster shapes.
How the Davies-Bouldin index evaluates clustering quality by finding each cluster's most similar neighbor — a pessimistic, worst-case metric that catches overlapping cluster pairs.
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.
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.
Interactive guide to convolution in CNNs: visualize sliding windows, kernels, stride, padding, and feature detection with step-by-step demos.
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
Understand dilated (atrous) convolutions: how dilation rates expand receptive fields exponentially without extra parameters and how to avoid gridding artifacts.
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 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.
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.
Learn Xavier (Glorot) initialization: how it balances forward signals and backward gradients to enable stable deep network training with tanh and sigmoid.
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.
Adaptive attention-based aggregation for graph neural networks - multi-head attention, learned weights, and interpretable graph learning
Understanding node importance through centrality measures, shortest paths, hop distances, clustering coefficients, and fundamental graph metrics
Learn Graph Convolutional Networks (GCN) with spectral theory, message passing, and node classification for geometric deep learning.
Learning low-dimensional vector representations of graphs through random walks, DeepWalk, Node2Vec, and skip-gram models
Hierarchical graph coarsening techniques - TopK, SAGPool, DiffPool, and readout operations for graph-level representations