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.
Explore machine learning concepts related to training. Clear explanations and practical insights.
Understand cross-entropy loss through interactive visualizations of probability distributions, gradient flow, and its connection to maximum likelihood estimation.
Explore the latent space of Variational Autoencoders through interactive visualizations of encoding, decoding, interpolation, and the reparameterization trick.
Understanding how gradients propagate through deep neural networks and the vanishing/exploding gradient problems.
Understanding layer normalization technique that normalizes inputs across features, making it ideal for sequence models and transformers.
Understanding the distribution shift problem in deep neural networks that batch normalization solves.
Understanding batch normalization technique that normalizes inputs to accelerate training and improve neural network performance.
Understanding skip connections, residual blocks, and their crucial role in training deep neural networks.