Author: Deep Learner

Hardware

Open source hardware and software platform to build a small scale self driving car

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions. Quick Links Use Donkey if you want to: Make an RC car drive its self. Compete in self driving races like DIY Robocars Experiment with autopilots, mapping computer vision and neural networks. Log sensor data. (images, user inputs, sensor readings) Drive your car via a web or game controller. Leverage community contributed […]

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Search

Cve-search : a tool to perform local searches for known vulnerabilities

cve-search is a tool to import CVE (Common Vulnerabilities and Exposures) and CPE (Common Platform Enumeration) into a MongoDB to facilitate search and processing of CVEs. The main objective of the software is to avoid doing direct and public lookups into the public CVE databases. Local lookups are usually faster and you can limit your sensitive queries via the Internet. cve-search includes a back-end to store vulnerabilities and related information, an intuitive web interface for search and managing vulnerabilities, a […]

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Python

Building Lists With Python’s .append()

Adding items to a list is a fairly common task in Python, so the language provides a bunch of methods and operators that can help you out with this operation. One of those methods is .append(). With .append(), you can add items to the end of an existing list object. You can also use .append() in a for loop to populate lists programmatically. In this course, you’ll learn how to: Work with .append() Populate lists using .append() and a for […]

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Document

An Indexer that works out-of-the-box when you have less than 100K stored Documents

An Indexer that works out-of-the-box when you have less than 100K stored Documents. U100K means under 100K. At 100K stored Documents with 768-dim embeddings, you can expect 300ms for single query or 20~120QPS for batch queries. Results are full Documents. U100KIndexer leverages jina.DocumenetArrayMemmap as the storage backend and .match() to conduct nearest neighbours search. It returns the full Documents as-is, hence no need to concatenate it with another key-value indexer to retrieve Documents. Pros & cons Pros Exhaustive search: highest […]

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Machine Learning

Federated Learning with Non-IID Data

This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.Federated Learning with Non-IID DataarXiv:1806.00582. Paper TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity in data distribution. In this paper, the authors propose a data-sharing strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge […]

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Automation

Capsule Network via Bottleneck Block and Automated Gradual Pruning

A pytorch implementation of “A Novel Effective and Efficient Capsule Network viaBottleneck Residual Block and Automated GradualPruning”. Since the experimental results in the paper are based on the graphics card (Tesla k40) with low computing power, the experiments at that time were very laborious, and there was no good display of the model results. The calculation of the graphics card (GTX 1060 and 3090) used in this reproduction is higher than the former. The results in the paper will fluctuate, […]

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Assistant

The app gets your sutitle.srt and proccess it to extract sentences

This app gets your sutitle.srt and proccess it to extract sentences, and also find Start time and End time of them. Step 1: install this packages using “PIP” googletrans (Translate) arabic_reshaper (Text correction) bidi.algorithm (Text correction) tkinter (GUI) Step 2: run SubtitleProccess.py Input: put srt file in src folder name should be “subtitle” => subtitle.srt Output: displaying sentences with order and structure Sentence is : .ﻝﺎﮕﻧﻮﮔ ﮏﻣ ﺭﻮﺴﻓﻭﺮﭘ ،ﺩﻮﺑ ﺪﯿﻫﺍﻮﺧ ﺎﺠﻨﯾﺍ ﺎﻤﺷ ﻪﮐ ﻢﺘﺴﻧﺍﺩ ﯽﻣ ﺪﯾﺎﺑ ﻦﻣ Start Time is […]

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Graph

Heterogeneous Graph Neural Networks for Keyphrase Generation

This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is built on the source code from keyphrase-generation-rl and fastNLP. Thanks for their work. If you use this code, please cite our paper: @inproceedings{ye2021heterogeneous, title={Heterogeneous Graph Neural Networks for Keyphrase Generation}, author={Ye, Jiacheng and Cai, Ruijian and Gui, Tao and Zhang, Qi}, booktitle={Proceedings of EMNLP}, year={2021} } Dependency python 3.5+ pytorch 1.0+ dgl 0.4.3 sentence_transformers 1.1.0 faiss 1.6.3

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Scripts

A simple Botter made in Python 3 for Kahoot.com

A simple Botter made in Python 3 for Kahoot.com.Also sorry for the shitty code lol.Cancel changes You need Python 3 installed on your device.After, do you need to install the requirements trought the file requirements.txt. Run this command in the same directory:pip3 install -r requirements.txt Then, just run the Tool with:python3 main.py Telegram: https://t.me/xssinjection || 1713081092 Discord: AsyncCode#4450 || 843362798486159371 Telegram: https://t.me/CyberLestofante Discord: kl3sshydra#6257 GitHub View Github    

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