Popular GitHub Repositories for Stacked Layers

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

simple neural network library in ANSI C

Acceleration package for neural networks on multi-core CPUs

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

kapre: Keras Audio Preprocessors

This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.

Shape and dimension inference (Keras-like) for PyTorch layers and neural networks

Official implementation for the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models"

Tensorflow implementation of Stacked Hourglass Networks for Human Pose Estimation

Official code for ReLoRA from the paper Stack More Layers Differently: High-Rank Training Through Low-Rank Updates

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

? A visualization of the CapsNet layers to better understand how it works

A flexible, adaptive classification system for dynamic text classification

Official implementation for "Stable Flow: Vital Layers for Training-Free Image Editing" [CVPR 2025]

Full pipeline for TianChi FashionAI clothes keypoints detection compitetion in TensorFlow

Simple Image Heads and Layers for deep learning based computer vision

Haste: a fast, simple, and open RNN library

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.