Metadata-Version: 2.1
Name: Dawnet
Version: 0.0.1
Summary: A deep learning package to inquire intelligence
Home-page: https://github.com/johntd54/dawnet
Author: _john
Author-email: trungduc1992@gmail.com
License: GNU General Public License (GPL)
Description: # Purpose
        
        **Make it easy to create, to trace, to debug, and to recreate models/algorithms.**
        
        What does that means:
        - Incorporation of common components (res-block, seblock,...) that can easily be used and constructed (which is good for model creations and modifications).
        - Data processing in functional form, which allows clear knowledge of what happens to the data before it is fed to the algorithms/models.
        - A tool to view and debug:
            + view model statistics
            + tinkering with the data and see what happens at the result (top-k result)
        - Inference must be constructed inside the model, with the input is the most basic data point (thinking of a complete stranger who needs to use your model, that person will not know anything about the nit-picks of your models and your data, they only have a data point and want to see the result coming out of your model)
        
        
        # Roadmap to usability
        
        - Session must work
        - Ability to get batch of data
        - Summarize session and model information
        - Implement mixup
        - Test all convs architecture
        
        
        # Model
        
        - A model should have the evaluate method ready (this part should be abstracted away from the progress / training procedure)
        - A model should have the load method ready (only for inference, because continual training requires knowledge about optimizer, training iteration)
        
        
        # Data
        
        Model is not the only part in creating intelligent system. Data plays a vital role in this process too. A lot of time, playing around with data, seeing how the model behaves when data is tweak a little bit can provide crucial insights for model improvement. Hence, data manipulation must be made easy to use.
Keywords: dawnet deep learning inquire artificial intelligence
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
