Metadata-Version: 2.1
Name: bluebird-stoick01
Version: 0.0.1
Summary: Deep learning library
Home-page: https://github.com/Stoick01/bluebird
Author: Gordan Prastalo
Author-email: gordan.prastalo.gp@gmail.com
License: MIT
Description: # BlueBird
        
        Simple deep learning library. 
        
        ## Usage
        
        Here is a simple implemetation of a model in bluebird.
        
        ```
        from bluebird.nn import NeuralNet
        from bluebird.activations import Relu, Softmax
        from bluebird.layers import Input, Dense
        from bluebird.data import BatchIterator
        from bluebird.loss import CategoricalCrossEntropy
        from bluebird.optimizers import SGD
        
        # create the neural net
        net = NeuralNet([
            Input(200), # input layer
            Dense(100, activation=Relu()),  # hidden layers with relu activation
            Dense(50, activation=Relu()),
            Dense(10, activation=Softmax()) # last hiddent layer with softmax activation
        ])
        
        # define optimizer and loss function
        net.build(optimizer=SGD(lr=0.003), loss=CategoricalCrossEntropy())
        
        # train your model
        net.fit(X_train, y_train, num_epochs=20)
        ```
        
        ## Roadmap
        
        There are a lot of updates planed, you will find comments throughout the library that define what features I'm planing to add in the future.
        
        ## Contribution
        
        Feel free to help, I know that there are many things that need to be optimized and implemented in the future, any help is welcome.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
