Awesome Open Source

Programming Languages

Search results for python backpropagation

backpropagation x

83 search results found

Pytorch Grad Cam ⭐ 8,723

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Pytorch Cnn Visualizations ⭐ 6,883

Pytorch implementation of convolutional neural network visualization techniques

Torchdiffeq ⭐ 4,882

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

Lectures Labs ⭐ 1,309

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

Backpack ⭐ 504

BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.

Grad Cam Pytorch ⭐ 438

PyTorch implementation of Grad-CAM, vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps

Dann_py3 ⭐ 298

python 3 pytorch implementation of DANN

Python Neural Network ⭐ 278

This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.

Greedy_infomax ⭐ 272

Code for the paper: Putting An End to End-to-End: Gradient-Isolated Learning of Representations

Visualisation tool for CNNs in pytorch

Neural Network From Scratch ⭐ 211

Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?

Make_a_neural_network ⭐ 196

This is the code for the "Make a Neural Network" - Intro to Deep Learning #2 by Siraj Raval on Youtube

Keras Lmu ⭐ 196

Keras implementation of Legendre Memory Units

SPINN (Stack-augmented Parser-Interpreter Neural Network): fast, batchable, context-aware TreeRNNs

Deeplearning_plantdiseases ⭐ 156

Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.

Build neural networks based only on Numpy

Machine_learning_2018 ⭐ 146

Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz

M3d Cam ⭐ 119

Torchcubicspline ⭐ 112

Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, and backpropagation.

Towards A Biologically Plausible Backprop ⭐ 102

Minimal Nn ⭐ 73

A minimal implementation of neural network for MNIST experiment. Used as an exercise to help understanding Backpropagation by implementing it in NumPy.

Theoretical Proof Of Neural Network Model And Implementation Based On Numpy ⭐ 66

This resource implements a deep neural network through Numpy, and is equipped with easy-to-understand theoretical derivation, mainly for the in-depth understanding of neural networks. 神经网络模型的理论证明与基于Numpy的实现。

Forward Thinking Pytorch ⭐ 66

Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

Rcnn_mdp ⭐ 59

Code base for solving Markov Decision Processes and Reinforcement Learning problems using Recurrent Convolutional Neural Networks.

Excitationbp ⭐ 59

Visualizing how deep networks make decisions

Bp Algorithm ⭐ 59

一文彻底搞懂BP算法：原理推导+数据演示+项目实战

Blur And Clear Classification ⭐ 56

Classifying the Blur and Clear Images

Stbp Simple ⭐ 55

A simple direct training implement for SNNs using Spatio-Temporal Backpropagation

Machine Learning In Python Workshop ⭐ 54

My workshop on machine learning using python language to implement different algorithms

Forward Warp ⭐ 52

An optical flow forward warp's lib with backpropagation using pytorch.

Domain_adaptation ⭐ 50

this project is the code of domain adaptation referenced by unsupervised domain adaptation by backpropagation(http://machinelearning.wustl.edu/m i realized it on mnist.

Artificialintelligenceengines ⭐ 48

Computer code collated for use with Artificial Intelligence Engines book by JV Stone

JAX-based Spiking Neural Network framework

Sparse Hyper ⭐ 44

Code for the paper "Learning sparse transformations through backpropagation"

Openset Da ⭐ 41

Code for 'Open Set Domain Adaptation by Backpropagation'

Stock Market Prediction Using Neural Network Models (Backpropagation, RNN LSTM, RBF) using keras with Tensorflow backend

Coursera Machine Learning In Python ⭐ 39

machine learning algorithms in python from scratch

Backpropagation_explained ⭐ 37

This is the code for "Backpropagation Explained" By Siraj Raval on Youtube

Proxprop ⭐ 35

Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Differentiation ⭐ 32

Implementing (parts of) TensorFlow (almost) from Scratch

Memformer ⭐ 30

Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

Biologically-Inspired and Machine Learning Algorithms written in Python

Substation ⭐ 28

Research and development for optimizing transformers

Pytorch implementation of TSSL-BP rule for Deep Spiking Neural Networks.

Hybrid Snn Conversion ⭐ 26

Training spiking networks with hybrid ann-snn conversion and spike-based backpropagation

Tensorflow Bptt ⭐ 25

Backpropagation-through-time library for TensorFlow

Gabornet ⭐ 24

Binarygan ⭐ 22

Code for "Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation"

Minimalistic Multiple Layer Neural Network From Scratch In Python ⭐ 21

Minimalistic Multiple Layer Neural Network from Scratch in Python.

Bohp_rnn ⭐ 20

Backprop training of recurrent neural networks with Hebbian plastic connections

Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.

Minineuralnets ⭐ 19

A framework for mini neural networks (perceptrons), written from scratch in python. The goal of the project is to demystify the workings of a neural network and various training algorithms by providing code written from scratch for the simplest neural network one could have.

Confidence ⭐ 19

New_grad ⭐ 18

A new lightweight auto-differentation library that directly builds on numpy. Used as a homework for CMU 11785/11685/11485.

Image_classification_cifar 10 ⭐ 18

Image Classification on CIFAR-10 Dataset using Multi Layer Perceptrons in Python from Scratch.

Backpropagation ⭐ 17

Neural Network Backpropagation Algorithm. Implementation using python

Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder and reinforcement learning. ICML 2019

Neural Networks Py ⭐ 12

Core neural networks framework supporting to build multilayer perceptron

Neuralnetwork ⭐ 11

numpy implementation of a feed forward neural network with backpropagation

Genetic Neural Network ⭐ 11

Training a Neural Network using Genetic Algorithm.

GRAPE with autograd on the TDSE and LME in Python

Brainprop ⭐ 10

BrainProp: How the brain can implement reward-based error backpropagation

Planning through backpropagation using TensorFlow.

Spikingtorch ⭐ 9

A pytorch implementation of spiking neural networks and backpropagation through spikes

Pso For Neural Nets ⭐ 9

Particle Swarm Optimizer For Neural Network Training

Automatic differentiation in python

Digit Recognition ⭐ 8

Training an ANN and a CNN to recognize handwritten digits using back-propagation algorithm on MNIST data-set

Repository for the free online book Oddly Satisfying Deep Learning from Scratch (link below!)

Devito4pytorch ⭐ 7

Integrating Devito operators into PyTorch

Feature Vis Yolov3 ⭐ 7

Feature visualization tool for YOLOv3, a real-time objection detection algorithm using a deep convolutional network with a Darknet backbone. Visualizes performance attributes via saliency maps to identify how features in the input pixel space influence our network’s predictions in terms of classification and localization

Neural_network_from_scratch ⭐ 7

Neural network/Back Propagation implemented from scratch for MNIST.从零开始实现神经网络和反向传播算法，识别MNIST

Simple Classifier By Back Propatation ⭐ 6

Implementing a simple classifier by numpy

All Optical Neural Networks ⭐ 6

Supporting code for "End-to-end optical backpropagation for training neural networks".

Keras Conv Vis ⭐ 6

Convolution visualization

Bidirectional ⭐ 6

Complete project for paper "Bidirectional Learning for Robust Neural Networks"

Gait Prop ⭐ 5

A biologically plausible learning rule derived from backpropagation of error

Hessian backpropagation (HBP): PyTorch extension of backpropagation for block-diagonal curvature matrix approximations

Model Based Rl ⭐ 5

Model-based Policy Gradients

Network Intrusion Detection System ⭐ 5

This is a software application to detect network intrusion by monitoring a network or system for malicious activity and predicts whether it is Normal or Abnormal(attacked with intrusion classes like DOS/PROBE/R2L/U2R).

Spikingmoyf ⭐ 5

Spiking Neural Network

Backpropagation Neural Network For Multivariate Time Series Forecasting ⭐ 5

Backpropagation Neural Network for Multivariate Time Series Forecasting (multi input single output: 2 inputs and 1 output)

Deepneuralnetworksfromscratch ⭐ 5

Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.

Adaptive_tbptt ⭐ 5

Code for Adapting TBPTT to Control Gradient Bias

Pytorch Dni ⭐ 5

Decoupled Neural Interfaces Using Synthetic Gradients - under develeopment

Exercise Breast Cancer ⭐ 5

Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.

Related Searches

Python Dataset (14,792)

Python Docker (14,113)

Python Machine Learning (14,099)

Python Tensorflow (13,736)

Python Deep Learning (13,092)

Python Jupyter Notebook (12,976)

Python Network (11,495)

Python Algorithms (10,033)

Python Testing (9,361)

Python Artificial Intelligence (8,580)

1-83 of 83 search results

Privacy | About | Terms | Follow Us On Twitter

Copyright 2018-2025 Awesome Open Source. All rights reserved.