marz869 Goto Github PK

.tmux

🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️

applied-deep-learning

Applied Deep Learning Course

avod

Code for 3D object detection for autonomous driving

calving_fronts_and_where_to_find_them

clean-code-slides

clr

eccv2022-multi-scale-and-cross-scale-contrastive-segmentation

Implementation of the methods described in "Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation", ECCV 2022

fastgcn

The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""

few-shot-gnn

frontlearning

Using neural networks to identify glacier calving fronts from satellite imagery

geemap

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.

gittutorial

graph-convolution-on-structured-documents

This repo contains code to convert Structured Documents to Graphs and implement a Graph Convolution Neural Network for node classification

ilqr

iterative Linear Quadratic Regulator with constraints.

infovislectures

learningratefinder

Find the best learning rate for a CNN with Keras. Source: www.pyimagesearch.com

manim

A community-maintained Python framework for creating mathematical animations.

mlalgorithms

Minimal and clean examples of machine learning algorithms

pythonbooks

scanned-document-classification-using-deep-learning

BFSI sectors deal with lots of unstructured scanned documents which are archived in document management systems for further use.For example in Insurance sector, when a policy goes for underwriting, underwriters attached several raw notes with the policy, Insureds also attach various kind of scanned documents like identity card, bank statement, letters etc. In later parts of the policy life cycle if claims are made on a policy, releted scanned documents also archeived.Now it becomes a tedious job to identify a particular document from this vast repository. The goal of this case study is to develop a deep learning based solution which can automatically classify scanned documents.

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

segment-everything-everywhere-all-at-once

Official implementation of the paper "Segment Everything Everywhere All at Once"

simgnn

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

sklearn-som

A simple, rectangular self-organizing map with methods similar to clustering methods in Scikit Learn.

som-tsp

Solving the Traveling Salesman Problem using Self-Organizing Maps

somoclu

Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters

tutorials

Code for all my tutorials