kmkolasinski/deep-learning-notes

Features

• Deep Learning Architectures - Provides modular code implementations and research notes for studying foundational and advanced neural network architectures.
• Neural Architecture and Training - Provides educational implementations of multilayer perceptrons and convolutional architectures for training and evaluation.
• Neural Network Implementations - Provides educational implementations of foundational and advanced neural architectures including autoencoders and neural ODEs.
• Computer Vision Features - Develops spatial pooling and capsule-based aggregation methods to improve visual feature representation.
• Sinkhorn Solvers - Calculates optimal transport between distributions using iterative Sinkhorn normalization of cost matrices.
• Feature Map Aggregators - Implements capsule-based feature aggregation to preserve spatial relationships and translational invariance in neural models.
• Machine Learning Resampling - Provides programmatic resampling techniques to balance minority class representation in training data pipelines.
• Machine Learning Optimization - Conducts experiments on the convergence properties of gradient-based methods and adaptive learning rates.
• Modular Architectures - Structures deep learning concepts into modular, isolated implementations for educational demonstration.
• Capsule Networks - Implements capsule-based pooling layers to explore alternative feature aggregation methods.
• Capsule Pooling Layers - Implements capsule-based pooling layers to aggregate feature map regions while preserving spatial relationships and translational invariance.
• Neural Network Stability Optimizations - Implements gradient normalization and weight regularization to improve the stability and convergence of neural models.
• Gradient Normalizers - Normalizes gradient vectors to decouple magnitude from direction, improving training stability.
• Weight Normalization - Implements weight normalization techniques to maintain statistical distributions and prevent vanishing gradients.
• Training Dataset Balancing - Balances training datasets by resampling minority classes to prevent bias during model learning.
• Weight Regularization - Regularizes neural weights to maintain self-normalizing properties in deep network layers.
• Training Data Pipelines - Implements data pipeline resampling to duplicate minority class samples for balanced training.
• Spatial Pooling - Performs dynamic feature pooling to preserve spatial representation and translational invariance.
• Deep Learning Education - Offers educational materials and implementations to help students understand neural network architectures and training techniques.
• Neural Network Implementations - Provides code examples demonstrating advanced training strategies including gradient normalization and capsule pooling.
• Deep Neural Network Training Optimization - Explores training stability through experiments with gradient normalization, weight regularization, and adaptive learning rates.
• Camera Trajectory Estimation - Analyzes sequential image frames to estimate camera trajectory and orientation in 3D environments.
• Optimal Transport Plan Computations - Computes optimal transport plans between data distributions through iterative matrix normalization.
• Physical Model Optimizers - Automates parameter scaling for physical tight-binding models to ensure design constraint satisfaction.

Star history