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image-processing-in-python

This repository contains the links to the article that I wrote on Medium pertaining to Image processing.

image-to-image-papers

🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)

image-to-latex

Convert images of LaTex math equations into LaTex code.

image2latex

A tool to convert math equation images to LaTeX markup

image_processing_with_python

Pillow: Image Processing with Python(Book Code)

image_segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

imageai

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

imagecaptioning.pytorch

I decide to sync up this repo and self-critical.pytorch. (The old master is in old master branch for archive)

imagededup

😎 Finding duplicate images made easy!

imageinary

Imageinary is a reproducible mechanism which is used to generate large image datasets at various resolutions. The tool supports multiple image types, including JPEGs, PNGs, BMPs, RecordIO, and TFRecord files

imageio

Python library for reading and writing image data

imagenode

Capture and Selectively Send Images and Sensor Data; detect Motion; detect Light

imagepy

Image process framework based on plugin like imagej, it is esay to glue with scipy.ndimage, scikit-image, opencv, simpleitk, mayavi...and any libraries based on numpy

imagezmq

A set of Python classes that transport OpenCV images from one computer to another using PyZMQ messaging.

imarispy

Python package for writing 5D numpy arrays to HDF5 files readable by both Imaris and BigDataViewer

imathics

Jupyter Kernel for Mathics

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbox

Python IMAP for Human beings

imf

Inferred Model-based Fuzzer

img.shields.io-old

Dynamic image server for Shields.io.

imgaug

Image augmentation for machine learning experiments.

imgviz

Image Visualization Tools (object detection, semantic and instance segmentation)

imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

imogen

GPU Texture Generator

implementing-the-backpropagation-learning-algorithm-using-l2-regularization

Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network. It is commonly used to train deep neural networks, a term referring to neural networks with more than one hidden layer. Backpropagation is a special case of an older and more general technique called automatic differentiation. In the context of learning, backpropagation is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also sometimes called backward propagation of errors, because the error is calculated at the output and distributed back through the network layers. The Backpropagation algorithm is a supervised learning method for multilayer feed-forward networks from the field of Artificial Neural Networks. Feed-forward neural networks are inspired by the information processing of one or more neural cells, called a neuron. A neuron accepts input signals via its dendrites, which pass the electrical signal down to the cell body. The axon carries the signal out to synapses, which are the connections of a cell’s axon to other cell’s dendrites. The principle of the backpropagation approach is to model a given function by modifying internal weightings of input signals to produce an expected output signal. The system is trained using a supervised learning method, where the error between the system’s output and a known expected output is presented to the system and used to modify its internal state. Technically, the backpropagation algorithm is a method for training the weights in a multilayer feed-forward neural network. As such, it requires a network structure to be defined of one or more layers where one layer is fully connected to the next layer. A standard network structure is one input layer, one hidden layer, and one output layer.

implementingclasses

Modules to help you get started Implementing your own classes in Python

import-mailbox-to-gmail

Import .mbox files into Google Workspace

importlab

A library that automatically infers dependencies for Python files

importlib-metadata-feedstock

A conda-smithy repository for importlib-metadata.

importlib_metadata