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.
7 min readConcept
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neural-networks
Explore machine learning concepts related to neural-networks. Clear explanations and practical insights.
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Concepts Found
Concepts Related to neural-networks
He/Kaiming Initialization
Learn He (Kaiming) initialization for ReLU networks: why ReLU needs special weight initialization, variance flow, and dead neurons explained.
7 min readConcept
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.
8 min readConcept
Batch Normalization in Deep Learning
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization.
7 min readConcept
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.
9 min readConcept