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It project was developed as a part of the programming exercises of the **Deep Learning** course offered by the [**Pattern Recognition Lab**](https://lme.tf.fau.de/) at **Friedrich-Alexander-Universität (FAU)**.\n\nThe codes are written in *Python* using object oriented programming concepts such as, *inheritance* or *polymorphism*. All fundamental layers, activation and loss functions, optimizers and regularizers are implemented by coding the corresponding mathematical operations using *NumPy* only, without the use of any deep learning frameworks. \n\n\n\n\u003cbr\u003e\n\n## Methods\nThe project is implemented in three parts as mentioned below. `NeuralNetwork.py` imports all layers and functions and runs them. `NeuralNetworkTests.py` contains unit tests for each and every layer and function to check if the implementation was properly done.\n\n\n\nThe main class running everything is `NeuralNetwork.py`. Various unit tests for every layer and function are included in `NeuralNetworkTests.py`.\n\nDetailed descriptions of implementation, along with methods and mathematical formulations can be found inside `Protocols`.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farkanivasarkar%2Fdeep-learning-from-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farkanivasarkar%2Fdeep-learning-from-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farkanivasarkar%2Fdeep-learning-from-scratch/lists"}