Create Nuxt extendable layer with this GitHub template.
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Parse CSS Cascade Layer names.
TensorFlow layers API in JavaScript
Use cascade layers in CSS
High Performance Layer 1 / Layer 2 Caching with Keyv Storage
A 4kb framework for creating sturdy frontend applications
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ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting
FirebaseAuth compatibility package that uses API style compatible with Firebase@8 and prior versions
This is the compatibility layer for the Firebase Analytics component of the Firebase JS SDK.
The primary entrypoint to the Firebase JS SDK
The Realtime Database component of the Firebase JS SDK.
Microsoft Application Insights Common JavaScript Library
This is a compatability layer for the Firebase Installations SDK
The CDK Construct Library for AWS Lambda in Python
AWS SDK for JavaScript Elastic Load Balancing V2 Client for Node.js, Browser and React Native
Dismissable layer utilities for the DOM
Generate llms.txt files to train large language models on your Starlight documentation website
Adobe Client Data Layer
Train a fast (FIFO) queue with a rollback mechanism. Behind the scenes it uses 2 arrays to simulate and perform fast shifting and popping operations without using the Array#shift() method..
Request tiles from WMS servers that support EPSG:3857
PostCSS plugin to add cascade layers to CSS
A validation & parsing library for TypeScript
Ruby Neural Network implementation using backpropagation and gradient descent for training
AgNet is a 2 Layer feed forward neural network with backpropogation for training. Can be used to approximate many functions and is useful for classification tasks such as character recognition.
GRNexus is a revolutionary cross-language AI platform that combines the elegance of Ruby with the raw power of native C acceleration. Train models in Ruby and deploy them in Python (or vice versa) with full compatibility. Features include 35+ activation functions, 12+ layer types, complete NLP pipeline, 40+ numeric operations, and intelligent training callbacks. 10-100x faster than pure Ruby implementations thanks to native C core.
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.
ignis-dl is the deep-learning layer of the Ignis ecosystem: NN modules (Linear, Embedding, LayerNorm, RMSNorm, Dropout), optimizers (SGD/Adam/AdamW), losses, and a transformer stack (multi-head + grouped-query attention, RoPE, SwiGLU, KV cache) with HuggingFace weight loaders (GPT-2, Llama). Loads real GPT-2 and Llama-3.2 checkpoints and matches HuggingFace logits, and trains transformers from scratch — in Ruby, on native Windows. Installing this pulls the whole stack (ignis + ignis-autograd), so it also serves as the meta-gem.
QA Robusta is an automation framework easing pain points away from automation test case writers. How is pain relieved? * Elements, such as links, buttons, and other html objects are defined in one location. This ensures over time the user won't have definitions spread out throughout different layers of code requiring time consuming updates if the application under test is modified. * Well defined flows allows the user to have a common means for navigating and controlling interactions with the application under test. This takes all logic out of test classes and yields in higher more modular code re-use. * When an application requiring testing has the elements and flows implemented less code savy resources can easily add new test cases once trained on how to access the flows and elements. * When ever a link or button is clicked a screen shot is taken * Results are available under site/results directory in html format. Report includes the rdoc on a per test class method along with any screen shots taken. Example report: https://cyberconnect.biz/opensource/demo_results.html * Transparent remote Unix command execution leading to well defined interfaces for common task. For example, one may have a class defined specifically for RemoteUnixNetwork. This class would have methods such as, assign_ip, ifup, ifdown, etc. This class then would be able to perform these task on any remote Unix machine. * Executes the same on Windows or Linux/Unix environments. Developers have the freedom to develop on the platform of choice. * Mechanize extension: Allows the user to define a web application's page elements in a YAML format and provide navigation paths accessing the YAML structure to interact with the web application. Users can also perform direct http.post or any other mechanize functionality when defining state-full interfaces to hit a web application without going through a browser.
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