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README.md

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 #  Introduction
 
-In order to promote the research and application of semi-supervised learning algorithms, we has developed LAMDA which is a convenient and practical semi-supervised learning toolkit. LAMDA-SSL has complete functions, convenient interfaces and detailed documentations. It integrates statistical machine learning algorithms and deep learning algorithms into the same framework. It is compatible with the popular machine learning toolkit sklearn and the popular deep learning toolkit pytorch.  It supports Pipeline mechanism and parameter search functions of sklearn and also supports GPU acceleration and distributed training functions of pytorch. At present, LAMDA-SSL contains 30 semi-supervised learning algorithms, including 12 algorithms based on statistical machine learning models and 18 algorithms based on deep learning models. LAMDA-SSL also contains 45 data processing methods used for 4 types of data: table, image, text, graph and 15 model evaluation criterias used for 3 types of task: classification, regression and clustering. LAMDA-SSL includes multiple modules such as data management, data transformation, algorithm application, model deployment and so on, which facilitates the implementation of end to end semi-supervised learning.
+In order to promote the research and application of semi-supervised learning algorithms, we has developed LAMDA-SSL which is a convenient and practical semi-supervised learning toolkit. LAMDA-SSL has complete functions, convenient interfaces and detailed documentations. It integrates statistical machine learning algorithms and deep learning algorithms into the same framework. It is compatible with the popular machine learning toolkit sklearn and the popular deep learning toolkit pytorch.  It supports Pipeline mechanism and parameter search functions of sklearn and also supports GPU acceleration and distributed training functions of pytorch. At present, LAMDA-SSL contains 30 semi-supervised learning algorithms, including 12 algorithms based on statistical machine learning models and 18 algorithms based on deep learning models. LAMDA-SSL also contains 45 data processing methods used for 4 types of data: table, image, text, graph and 15 model evaluation criterias used for 3 types of task: classification, regression and clustering. LAMDA-SSL includes multiple modules such as data management, data transformation, algorithm application, model deployment and so on, which facilitates the implementation of end to end semi-supervised learning.
 
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 <img width="1000px" src="./Imgs/Overview.png">