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Possibly pretrained for certain tasks.\r\n* Standard datasets to test models. Development of new models can be difficult. Standard datasets eliminates errors in the data and lets you focus on developing the model. \r\n  * Classification\r\n  * Regression\r\n  * Masking (eg. arrival picking)\r\n* Augmentation methods.\r\n\r\n## Installation\r\n``pip install nais``\r\n\r\nIf an error occurs when importing `nais`, likely ``sndfile`` library is not installed (check the error), and needs to be:\r\n\r\n``apt-get install libsndfile1-dev``\r\n\r\n# Quick example\r\n\r\n```python\r\nimport numpy as np\r\nfrom nais.Models import AlexNet1D\r\n\r\nX = np.random.normal(size=(16,256,3)) #16 examples of three channel data.\r\ny = np.random.randint(0,1,size=(16,)) #Labels\r\n\r\nmodel = AlexNet1D(num_outputs=1) #binary \r\nmodel.compile('adam','binary_crossentropy')\r\nmodel.fit(X,y)\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnorsar-official%2Fnais","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnorsar-official%2Fnais","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnorsar-official%2Fnais/lists"}