Matrix convolution: It offers the direct and the fourier transform convolution
Convolution using the FFT or standard algorithm
Simple direct and extendable convolution library.
PixiJS filter to apply a convolution effect
Simple, unoptimized 1D and 2D convolution functions for typed-arrays
Canvas convolution filters
Typedarray integer & float pixel buffers w/ customizable formats, blitting, drawing, convolution
Extensible bitmap image convolution, kernel presets, normal map & image pyramid generation
Iterable convolution for JavaScript
Convolution and cross correlation functions for ndarrays
calculate square matrix - gaussian blur convolution kernel
<div align="center"> <img width="200" height="200" src="https://s3.amazonaws.com/pix.iemoji.com/images/emoji/apple/ios-11/256/crayon.png"> <h1>@jimp/utils</h1> <p>Utils for jimp extensions.</p> </div>
Image sharpening using convolution
WASM Webcomponent fir- for digital signal processing FIR convolution following open-wc recommendations
Functional, polymorphic API for 2D geometry types & SVG generation
GPU Javascript Library for Machine Learning
0D/1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage
**GPU accelerated image resizer**
Savitzky–Golay filter in Javascript
A dynamic image editor, image viewer, 1D/2D Barcode Generator and 1D/2D Barcode Decoder nodes
Digital filter design & processing: IIR, FIR, smoothing, adaptive, multirate
Next2D Filters Package
Image processing module
Fast Barnes interpolation for irregularly spaced 1D, 2D and 3D sample data in TypeScript.
surge synthesizer -- surge super oscillator (SSO)
OpenCL-accelerated 2D convolutions
N-Dimension convolution (with FFT) lib for ndarray.
Fast, minimal dependency, completely Rust implementation of convolutions for machine learning.
Easy and extensible pure rust image convolutions
A library for simulating the Lenia system of cellular automata.
CNN feature extraction for image embeddings with SIMD acceleration
surge synthesizer -- emphasize effect
1-dimensional convolution library intended for use in DSP applications.
Parallel image convolution on GPU.
Neural networks from scratch, in Rust.
Fast convolution and impulse-response extraction for audio applications
Convolution for NArray
Simplification of convolver gem, FFTW removed, suitable only for smaller kernels. Convolver gem author is Neil Slater, slobo777@gmail.com, https://github.com/neilslater
This is not even remotely finished or even started on. Please don't download.
Allows design of digital signals using the FFT, design of Digital Filters using the Windowing Method, creation of Digital Signals or Analog Signals sampled at a certain interval, convolution, cross-correlation, and visualization of the data. .
Shattered View uses Ogre and Ruby to create an accessible interface to normally convoluted common game tasks.
RubyBreaker is a dynamic type documentation/checking tool for Ruby. It dynamically instruments code, monitors objects during execution, performs dynamic type checking, and generates type documentation based on the profiled information. RubyBreaker helps Ruby programs "break" out of obscurities and convolutions by auto-documenting type information.
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.
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