Batch Normalization: Mini-Batch Normalization
Batch Normalization stabilizes and accelerates deep network training by normalizing activations. Complete guide with PyTorch code.
Batch Normalization: Mini-Batch Normalization LireLearn Python
Explore in-depth articles on programming, analyses of innovative GitHub projects, and guides on advanced Python frameworks and techniques.
Gaussian Processes: Non-Parametric Bayesian Regression
Gaussian Processes model distributions directly over functions for regression with uncertainty. Complete guide with Python code.
Gaussian Processes: Non-Parametric Bayesian Regression Lire
Gumbel-Softmax: Differentiable Discrete Sampling
Gumbel-Softmax enables differentiation through discrete sampling. Complete guide with reparameterization trick and PyTorch code.
Gumbel-Softmax: Differentiable Discrete Sampling Lire
Contrastive Predictive Coding: Contrastive Prediction in Latent Space
CPC learns representations by predicting the future in a contrastive latent space. Complete guide with InfoNCE and PyTorch code.
Contrastive Predictive Coding: Contrastive Prediction in Latent Space Lire
Pooling Layers: Aggregation Layers in CNNs
Pooling layers reduce spatial dimensions while preserving important features. Complete guide with max, average, adaptive and PyTorch code.
Pooling Layers: Aggregation Layers in CNNs Lire
Spatial Transformer Networks: Learned Spatial Invariance
STNs learn to transform images (crop, rotate, deform) in a differentiable way to improve CNN robustness. Complete guide with PyTorch.
Spatial Transformer Networks: Learned Spatial Invariance Lire
Online Learning: Continuous Stream Learning
Online Learning updates the model with each new data point without retraining. Complete guide with Passive-Aggressive and Python code.
Online Learning: Continuous Stream Learning Lire
Kernel Methods: SVM Kernel and Kernel PCA
Kernel methods implicitly project data into a high-dimensional space to solve nonlinear problems. Complete guide with Python code.
Kernel Methods: SVM Kernel and Kernel PCA Lire
Continual Learning: Continuous Learning Without Forgetting
Continual Learning enables models to learn new tasks without forgetting old ones. Complete guide with EWC, replay, and Python code.
Continual Learning: Continuous Learning Without Forgetting Lire
Label Propagation: Label Propagation on Graphs
Label Propagation diffuses known labels through a similarity graph to classify unlabeled data. Complete guide with Python code.
Label Propagation: Label Propagation on Graphs Lire