Return the strides of a provided ndarray.
Return the strides of a provided ndarray.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Reorder ndarray dimensions and associated strides for loop interchange.
Compute the minimum and maximum linear indices in an underlying data buffer which are accessible to an array view.
Determine if a buffer length is compatible with provided ndarray meta data.
Convert a linear index in an array view to a linear index in an underlying data buffer.
Determine the order of a multidimensional array based on a provided stride array.
Given a stride array, determine array iteration order.
Determine the index offset which specifies the location of the first indexed value in a multidimensional array based on a stride array.
Convert a linear index to an array of subscripts.
Apply a nullary callback and assign results to elements in a strided output array.
Multidimensional array constructor.
Base multidimensional array.
Apply a binary callback to elements in strided input arrays and assign results to elements in a strided output array.
Convert an ndarray buffer to a generic array.
A modern design system built with React, TypeScript, TailwindCSS, and Storybook
Given a stride array, determine whether an array is row-major.
Generate a stride array from an array shape.
Async-first terminal UI spinners and progress bars
Cache-optimized kernels for strided multidimensional array operations in Rust (ported from Julia Strided.jl/StridedViews.jl).
Idiomatic Rust wrappers for the NVIDIA CUDA stack (Driver API, Runtime API, NVRTC, cuBLAS, cuDNN, NCCL, NVML, ...). Umbrella crate.
A strided slice type
Stride manipulation (as_strided, sliding_window_view) for ferray
Device-agnostic strided view types and metadata operations (ported from Julia StridedViews.jl).
Dress your pixels for the occasion — SIMD-optimized pixel format conversions
Stride manipulation (as_strided, sliding_window_view) for ferrum
Shared traits for strided-rs: element operations, scalar bounds, and type-level composition.
Cache-efficient tensor permutation / transpose (HPTT-inspired).
Tensor IR for numeric optimization in BHC
Detect regular tile grids in images (sprite sheets, tile atlases, game maps) and recover the tileset, via 1D autocorrelation.
Ruby wrapper for the Stride API
Implements the time expression which strided date.
This gem is a Logstash plugin required to be installed on top of the Logstash core pipeline using $LS_HOME/bin/logstash-plugin install gemname. This gem is not a stand-alone program
Notify to Stride about deployments
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.