Open Source Python Neural Network Libraries

Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data.

This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested under GPU and python3. But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments.