Gradient

llama-index llms gradient integration

Calculations

GradNorm - Pytorch

High throughput asynchronous reinforcement learning framework

Python package + CLI to generate stylistic wordclouds, including gradients and icon shapes!

Automatic-Class-Balanced MSE Loss for PyTorch (ACB-MSE) to combat class imbalanced datasets and stabilise fluctuating loss gradients.

A package for applying differential privacy to model training using gradient shuffling and membership inference attack detection.

ASDL: Automatic Second-order Differentiation (for Fisher, Gradient covariance, Hessian, Jacobian, and Kernel) Library

Polynomial approximations

A package for performing stochastic gradient descent (arXiv:1710.04626) to layout graphs

A library which Predicts the Y value(output) for given test data points using gradient descent algorithm.

A neural network architecture for building fully explainable neural network for arithmetic and gradient logic expression approximation.

Proximal Gradient Methods for Pytorch

Gradient boosting libraries integrated with pytorch

ADAO: A module for Data Assimilation and Optimization

A wrapper of scipy minimize with automatic gradient and hessian computation.

StructureBoost is a Python package for gradient boosting using categorical structure. See documentation at: https://structureboost.readthedocs.io/

Python Image Displacement Identification.

A library which Predicts the Y value(output) for given test data points using gradient descend algorithm.

A JAX-based L-BFGS optimizer

A package for evaluating dimensionality reduction algorithms

Makes learning gradient descent easy

Neural Network Gradient Metrics with PyTorch

A python package containing implementations of two widely used algorithms to minimise any function - Gradient Descent algorithm and the Normal Equation method.

Gradient boosted decision tree palindrome predictor, used to locate regions for further investigation thru http://palindromes.ibp.cz/

Official implementation of Conflict-free Inverse Gradients method

growingnn is a cutting-edge Python package that introduces a dynamic neural network architecture learning algorithm. This innovative approach allows the neural network to adapt its structure during training, optimizing both weights and architecture. Leveraging a Stochastic Gradient Descent-based optimizer and guided Monte Carlo tree search, the package provides a powerful tool for enhancing model performance.

Medical Deep Learning Framework

Method PROTES (PRobabilistic Optimizer with TEnsor Sampling) for derivative-free optimization of the multidimensional arrays and discretized multivariate functions based on the tensor train (TT) format

Medical Deep Learning Framework

A package compatible with scikit-learn.

GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network

PulPy: Pulses in Python. A package for MRI RF and gradient pulse design.

A python machine learning library to use and visualize gradient descent for linear regression and logistic regression optimization.

RNN and general weights, gradients, & activations visualization in Keras & TensorFlow

Medical Deep Learning Framework.

An ensemble xgboost model made by bright-rookie.

PyTorch optimizer based on nonlinear conjugate gradient method

WAsserstein Global Gradient-free OptimisatioN (WAGGON) methods library.

Magnetic Field Coil Generator for Python.

Stochastic optimization routines for Theano

Field and gradient calculations for magnetic coils

NFNets, PyTorch

A python package for the Cyclic Gradient Boosting Machine algorithm

calculate gravity anomalies from multi-interfaces and estimate double-interfaces model from gravity anomalies, given prior information about constraints between the density interfacesthe using Crust1.0 model, where Vertical Gravity Gradient data are used asinputs

Python Sensitivity Analysis - Gradient DataFrames and Hex-Bin Plots

FUNGI: Features from UNsupervised GradIents

Fast (and cheeky) differentially private gradient-based optimisation in PyTorch

Functions and classes for gradient-based robot motion planning, written in Ivy.