MalayAgr/DeepNeuralNetworksFromScratch
Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.
This project helps deep learning practitioners understand how neural networks are built from the ground up. It provides working examples of common layers, activations, and optimizers using fundamental Python and NumPy code. Aspiring machine learning engineers and researchers can use this to grasp the underlying mechanics before diving into high-level frameworks.
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Use this if you are a deep learning student or educator looking to understand the mathematical and computational mechanics of neural networks without relying on abstracted libraries.
Not ideal if you are a practitioner looking for a high-performance deep learning framework for building and training complex models efficiently, as it lacks automatic differentiation and advanced optimizations.
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Language
Python
License
MIT
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Last pushed
Dec 12, 2022
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