amazing-nlp | Eventually we 'll build an universal deep NLP toolkits

 by   yangchiu Python Version: Current License: No License

kandi X-RAY | amazing-nlp Summary

kandi X-RAY | amazing-nlp Summary

amazing-nlp is a Python library. amazing-nlp has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

use simple linear regression to fit a line. apply logistic regression to MNIST digit recognition. apply convolution neural network to MNIST digit recognition. apply convolution neural network to CIFAR-10 object recognition. use recurrent neural network to reconstruct sine wave. apply multi-layers LSTM to milk production time-series prediction. use autoencoder as PCA to perform dimension reduction from 3D to 2D. use autoencoder as PCA to perform dimension reduction from 30D to 2D. use autoencoder to generate new training data for MNIST. use genarative adversarial network to generate new training data for MNIST. apply fully-connected network to wine classification with sparse categorical cross entropy. apply fully-connected network to iris classification with categorical cross entropy. investigate the relations between output, hidden states, cell states of LSTM and BiLSTM and their shapes. investigate the relations between output and hidden states of GRU and BiGRU and their shapes. load pre-trained word2vec model into gensim. load pre-trained Glove model into numpy array. use bag-of-words and pre-trained Glove to perform text classification. train a new word2vec model using Tensorflow's nce-loss. train a new word2vec model using Tensorflow, but doesn't use nce-loss, do the negative sampling ourselves instead. use gensim API to train a new word2vec model. implement and train a new Glove model using Tensorflow. perform name-entity recognition using Tensorflow. perform name-entity recognition using Keras. train a language model based on Moby Dick, and generate new text sequences using this model the input length of the model is fixed, so when we do the sampling, we need 0 ~ T inputs to generate T + 1 output. use LSTM and pre-trained Glove to perform text classification. prove that not only RNN, CNN can be used on time-series data and perform text classification, too. apply a simple memory network to bAbI dataset, build a model can give a yes/no answer based on the given story and question. prove that not only CNN, RNN can be used on image data and perform digit recognition, too. train a language model based on Robert Frost poetry, and generate new peotry sequences using this model we create a model with input length 1 besides the trained model (which has input length T) so when we do the sampling, we only need T input to generate T + 1 output.
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            kandi-support Support

              amazing-nlp has a low active ecosystem.
              It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
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              It had no major release in the last 6 months.
              amazing-nlp has no issues reported. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of amazing-nlp is current.

            kandi-Quality Quality

              amazing-nlp has no bugs reported.

            kandi-Security Security

              amazing-nlp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              amazing-nlp does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              amazing-nlp releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.

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            Install amazing-nlp

            You can download it from GitHub.
            You can use amazing-nlp like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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            gh repo clone yangchiu/amazing-nlp

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            git@github.com:yangchiu/amazing-nlp.git

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