VAE Latent Space: Understanding Variational Autoencoders
Explore VAE latent space in deep learning. Learn variational autoencoder encoding, decoding, interpolation, and the reparameterization trick.
6 min readConcept
Tagged with
training
Explore machine learning concepts related to training. Clear explanations and practical insights.
11
Concepts Found
Concepts Related to training
Dropout Regularization
Understand dropout regularization: how randomly silencing neurons prevents overfitting, the inverted dropout trick, and when to use each dropout variant.
10 min readConcept
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.
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
Gradient Flow in Deep Networks
Learn how gradients propagate through deep neural networks during backpropagation. Understand vanishing and exploding gradient problems with interactive visualizations.
8 min readConcept
DataParallel vs DistributedDataParallel
Compare PyTorch DataParallel vs DistributedDataParallel for multi-GPU training. Learn GIL limitations, NCCL AllReduce, and DDP best practices.
6 min readConcept
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.
6 min readConcept
Layer Normalization
Learn layer normalization for transformers and sequence models: how normalizing across features enables batch-independent training with interactive visualizations.
7 min readConcept
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.
8 min readConcept
Batch Normalization
Learn batch normalization in deep learning: how normalizing layer inputs accelerates training, improves gradient flow, and acts as regularization with interactive visualizations.
7 min readConcept
Skip Connections
Learn how skip connections and residual learning enable training of very deep neural networks. Understand the ResNet revolution with interactive visualizations.
9 min readConcept