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.
9 min readConcept
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training
Explore machine learning concepts related to training. Clear explanations and practical insights.
10
Concepts Found
Concepts Related to training
VAE Latent Space: Understanding Variational Autoencoders
Explore the latent space of Variational Autoencoders through interactive visualizations of encoding, decoding, interpolation, and the reparameterization trick.
6 min readConcept
He/Kaiming Initialization
Master He (Kaiming) initialization, the optimal weight initialization technique for ReLU networks that prevents gradient vanishing in deep neural architectures.
9 min readConcept
Xavier/Glorot Initialization
Understand Xavier (Glorot) initialization, the weight initialization technique that maintains signal variance across layers for stable deep network training.
7 min readConcept
Dropout Regularization
Master dropout, the powerful regularization technique that prevents overfitting by randomly deactivating neurons during training, creating an ensemble of sub-networks.
11 min readConcept
Gradient Flow in Deep Networks
Understanding how gradients propagate through deep neural networks and the vanishing/exploding gradient problems.
8 min readConcept
Layer Normalization
Understanding layer normalization technique that normalizes inputs across features, making it ideal for sequence models and transformers.
5 min readConcept
Internal Covariate Shift
Understanding the distribution shift problem in deep neural networks that batch normalization solves.
7 min readConcept
Batch Normalization
Understanding batch normalization technique that normalizes inputs to accelerate training and improve neural network performance.
5 min readConcept
Skip Connections
Understanding skip connections, residual blocks, and their crucial role in training deep neural networks.
4 min readConcept