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

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