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.
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Deep Learning Fundamentals
Core concepts and building blocks of deep neural networks.
17
Concepts
All Deep Learning Fundamentals Concepts
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.
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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.
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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.
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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.
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VAE Latent Space: Understanding Variational Autoencoders
Explore the latent space of Variational Autoencoders through interactive visualizations of encoding, decoding, interpolation, and the reparameterization trick.
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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.
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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.
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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.
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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: 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.
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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.
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Gradient Flow in Deep Networks
Understanding how gradients propagate through deep neural networks and the vanishing/exploding gradient problems.
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Layer Normalization
Understanding layer normalization technique that normalizes inputs across features, making it ideal for sequence models and transformers.
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Internal Covariate Shift
Understanding the distribution shift problem in deep neural networks that batch normalization solves.
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Batch Normalization
Understanding batch normalization technique that normalizes inputs to accelerate training and improve neural network performance.
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Skip Connections
Understanding skip connections, residual blocks, and their crucial role in training deep neural networks.
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