Back Propagation Neural Network in C++
-
Updated
Jun 9, 2024 - C++
Back Propagation Neural Network in C++
Implementation of the "Applying the Forward-Forward Algorithm to the Event-Based Sensing" paper.
JAX compilation of RDDL description files, and a differentiable planner in JAX.
Coursework on Introduction to Machine Learning - CS M146
Python implementation of a Feed-Forward Backpropagation neural network using only the standard library
Custom implementation of a neural network from scratch using Python
Scalar value autograd engine and a neural network library similar to a PyTorch-like API.
A small deep learning framework written from scratch in python. Implements forward and backward propogation by hand. For those who are interested in learning how to do so.
A simple neural network for identifying handwritten digits using no high-level ml libraries (includes lots of math exposition)
NYCU Deep Learning Spring 2024
This repository contains my original code solutions and project reports, as well as the provided problem formulations for assignments 1, 2, 3 and 6 for the Natural Language Processing course, held in the Autumn semester 2022 at ETH Zürich. Each folder contains the files of the corresponding assignment.
Lightweight Python package for automatic differentiation
A modular C++17 framework for automatic differentiation
This project will give some highlight on the notion of F-adjoint which has been recently introduced in the following arxiv preprint: "Backpropagation and F-adjoint. arXiv preprint arXiv:2304.13820".
A basic neural network with backpropagation programmed from scratch in C++
This repository contains notes, slides, labs, assignments and projects for the Deep Learning Specialization by DeepLearning.AI and Coursera.
A simple, lightweight and powerful ML framework for Lua.
Add a description, image, and links to the backpropagation topic page so that developers can more easily learn about it.
To associate your repository with the backpropagation topic, visit your repo's landing page and select "manage topics."