论文代写 作业代写转为AI自动运作项目

收集各大兼职网站 QQ接单群 闲鱼的项目描述 人工标注项目报价费用 训练实现自动报价抽成机器人

根据项目描述和大量公开数据 进行开放式问答 训练并应用于论文实现代写方面的付费问答机器人

ctfwiki’s intro on CGC

analyze source code first, then plan attack or fix code

cgc’s github repo and website

search for darpa cgc on github

cyber-challenge Some toy examples, to demonstrate ideas that could be used in DARPA’s Cyber Grand Challenge including modifying java bytecode and filter out html requests on the fly

EVIL (Exploiting software VIa natural Language) is an approach to automatically generate software exploits in assembly/Python language from descriptions in natural language. The approach leverages Neural Machine Translation (NMT) techniques and a dataset that we developed for this work.

Topics

linux exploit encoder assembly decoder dataset seq2seq shellcode nmt software-exploitation codebert

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Readme

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GPL-3.0 license

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Contributors 2

@piliguori

piliguori Pietro Liguori

@taisazero

taisazero Erfan Al-Hossami

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Speech Recognition using MFCC and HMM

MindsDB

documentation

cloud mindsdb editor

warning: this thing could break your dependencies. better use docker instead.

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docker pull mindsdb/mindsdb
# pip3 install mindsdb

HMMLearn (unsupervised)

most useful feature:

training and inferring the hidden states

supervised hmm learning

seqlearn

pomegranate (both supervised and unsupervised)

documentation

All models that support labeled data support semi-supervised learning, including naive Bayes classifiers, general Bayes classifiers, and hidden Markov models.

While probability Distributions are frequently used as components of more complex models such as mixtures and hidden Markov models, they can also be used by themselves. Many data science tasks require fitting a distribution to data or generating samples under a distribution. pomegranate has a large library of both univariate and multivariate distributions which can be used with an intuitive interface.

General Mixture Models (GMMs) are an unsupervised probabilistic model composed of multiple distributions (commonly referred to as components) and corresponding weights. This allows you to model more complex distributions corresponding to a singular underlying phenomena. For a full tutorial on what a mixture model is and how to use them, see the above tutorial.

Hidden Markov Models

Bayes Classifiers and Naive Bayes

Markov Chains

Bayesian Networks

Markov Networks

provide tasks and give the best model for given task

i’m afraid paperswithcode.com takes huge amount of time for one to categorize and classify. better get basic task categories out before you need/search it.

scrapers

kaggle leaderboard

kaggle leaderboard scraper

platforms

aistudio.baidu.com

paperswithcode.com

kaggle.com

tasks

datasets for deeplearning, classification and evaluation

100 audio and video datasets

video datasets

youtube8m

jax

docs

autograd and xla (Accelerated Linear Algebra)

pyro

probabilistic programming

getting started

examples

sample code

numpyro

getting started

pyro implementation in numpy, alpha stage

scikit-learn

machine learning in python

libsvm

install official python bindings:

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pip install -U libsvm-official

third-party python libsvm package installed by:

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pip install libsvm

opennlp

hands-on docs

model zoo

opennlp uses onnx runtime(maybe?), may support m1 inference.

opennlp is written in java. after installing openjdk on macos with homebrew, run this to ensure openjdk is detected:

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sudo ln -sfn $(brew --prefix)/opt/openjdk/libexec/openjdk.jdk /Library/Java/JavaVirtualMachines/openjdk.jdk

opennlp has a language detector for 103 languages, including chinese. opennlp has a sentence detector (separator) which could be trained on chinese (maybe?)

in order to use opennlp with less code written, here’s how to invoke java from kotlin

dl4j

found on mannings article about better search engine suggestions. in this example it is used with lucene, which has image retrieval (LIRE) capability. lucene is also avaliable as lucene.net in dotnet/c#.

to install lucene.net:

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dotnet add package Lucene.Net --prerelease

deep learning library for java

xgboost

gradient boost is used to train decision trees and classification models.

lightgbm

Light Gradient Boosting Machine

have official commandline tools. installation on macos:

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brew install lightgbm

install python package on macos:

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brew install cmake
pip3 install lightgbm

pymc

examples

if want to enable jax sampling, install numpyro or blackjax via pip

difference between pymc3 (old) and pymc (pymc4):

pymc is optimized and faster than pymc3

pymc3 use theano as backend while pymc use aesara (forked theano)

docs with live demo of pymc

PyMC is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using Markov chain Monte Carlo (MCMC) methods.

fastai

a high level torch wrapper including “out of the box” support for vision, text, tabular, and collab (collaborative filtering) models.

docs

courses

on the twitter list related to opennlp shown up on its official website, fastai has been spotted.

fastai does not support macos. or is it? fastai is on top of pytorch. initial support starts with 2.7.8 and now it is currently 2.7.9

searching ‘samoyed’ like this in github we get a dataset for pets classification called imagewoof from fastai 2020 tutorial series. more image classes like subcategories of cats may be found in imagenet.