This repository contains the code that produces the numeric section in On the Use of TensorFlow Computation Graphs in combination with Distributed Optimization to Solve Large-Scale Convex Problems

To become a comprehensive platform for users to execute data processing and AI model training tasks using Ocean nodes within the C2DV2 architecture.

Projeto de TCC baseado em processamento de imagens e visão computacional para identificar a evolução de ogras da engenharia civil

Automatic derivative calculation of scalar functions.

Code for projects and homework from ECE-491: Computational Graphs for Machine Learning (aka Deep Learning/Neural Nets)

These are my computer graphics projects.

Assignment 2: GPU Executor

DAG-based computation graph for streaming data

Creating and analyzing interaction graphs based on boolean functions

Assignment 1: automatic differentiation

Implementation of automatic differentiation (AD) in forward and backward modes with mathematical explanations

Python library for developing data processing algorithms as computational graphs and their integration with publish-subscribe systems

An easy-to-use dynamic computation graph library for running e2e ML training.

Java library for building computation graphs.

Computation graphs, automatic differentiation and machine learning for Kotlin

a single c++ file for learing how data flow graphs work.

Real-time execution and remote monitoring and tuning of BDSim Block-Diagrams for modeling and control of Dynamical Systems

C computation graph, AutoGrad with OpenCL support [WIP]

artifax is a Python package to evaluate nodes in a computation graph where the dependencies associated with each node are extracted directly from their function signatures.

A short collection of Jupyter notebooks explaining some basic computational math