A tool to prepare websites grabbed with wget for local viewing.

Last update: Apr 23, 2022

Related tags

Deep Learning makelocal

Overview

makelocal

A tool to prepare websites grabbed with wget for local viewing.

exapmples

After fetching xkcd.com with:

wget -r -no-remove-listing -r -N --page-requisites --adjust-extension -HD xkcd.com xkcd.com

You can run this command convert links in the HTML documents to refrence your local version.

find xkcd.com | grep '\.html' | grep -v '\.bak'| python3 convert.py -b `pwd`

Usage

usage: convert.py [-h] [-b] [-o] directory

Convert links in html files for local viewing. file names are read from stdin.

positional arguments:
  directory       The absolute location of the files.

options:
  -h, --help      show this help message and exit
  -b, --backup    Backup origninal files, recommended for first run
  -o, --original  Used backed up files, recommended for later runs

-b save the original file to [filename].bak this is usefull to avoid having to grab a fresh tree if you mess something up or move the files.

Owner

aka: BuggyBugs

GitHub Repository

Generative Adversarial Text to Image Synthesis

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Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".

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Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

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Code to compute permutation and drop-column importances in Python scikit-learn models

Feature importances for scikit-learn machine learning models By Terence Parr and Kerem Turgutlu. See Explained.ai for more stuff. The scikit-learn Ran

537 Dec 31, 2022

Official implementation of Protected Attribute Suppression System, ICCV 2021

6 Jan 01, 2023

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57 Dec 11, 2022

Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Dataloader for CIFAR-N CIFAR-10N noise_label = torch.load('./data/CIFAR-10_human.pt') clean_label = noise_label['clean_label'] worst_label = noise_lab

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Sample code from the Neural Networks from Scratch book.

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Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

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232 Dec 28, 2022

A tool to prepare websites grabbed with wget for local viewing.

Related tags

Overview

makelocal

exapmples

Usage

Owner

Generative Adversarial Text to Image Synthesis

Data manipulation and transformation for audio signal processing, powered by PyTorch

Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Awesome Human Pose Estimation

TAPEX: Table Pre-training via Learning a Neural SQL Executor

Code to compute permutation and drop-column importances in Python scikit-learn models

Official implementation of Protected Attribute Suppression System, ICCV 2021

Referring Video Object Segmentation

Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Sample code from the Neural Networks from Scratch book.

Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

A fast python implementation of Ray Tracing in One Weekend using python and Taichi

Code and description for my BSc Project, September 2021

A Python Package for Convex Regression and Frontier Estimation

Springer Link Download Module for Python

QAT(quantize aware training) for classification with MQBench

BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)

Simple Tensorflow implementation of Toward Spatially Unbiased Generative Models (ICCV 2021)