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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Deep Learning Fundamentals
Core concepts and building blocks of deep neural networks.
25
Concepts
All Deep Learning Fundamentals Concepts
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.
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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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Feature Pyramid Networks
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
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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.
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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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Contrastive Loss
Understand contrastive loss for representation learning: interactive demos of InfoNCE, triplet loss, and embedding space clustering with temperature tuning.
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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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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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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.
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KL Divergence
Learn KL divergence for machine learning: measure distribution differences in VAEs, knowledge distillation, and variational inference with interactive visualizations.
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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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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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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.
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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.
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Prompt Engineering
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques with interactive visualizations.
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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.
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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.
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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 with interactive visualizations.
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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
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training with interactive visualizations.
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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.
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Batch Normalization
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization with interactive visualizations.
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Skip Connections
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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