rohan-paul/Deep-Learning-Paper-Implementation
From Scratch Implementation of some popular Deep Learning Papers with PyTorch and Tensorflow
This project provides practical, from-scratch code implementations of popular deep learning research papers. It takes complex theoretical papers and translates them into working PyTorch and TensorFlow code, allowing deep learning practitioners, researchers, and students to understand and apply advanced AI models without starting from zero.
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Use this if you are a deep learning practitioner, researcher, or student who wants to understand how cutting-edge AI models from academic papers are built and implemented in code.
Not ideal if you are looking for a plug-and-play solution or a high-level library to apply deep learning without diving into the underlying code and theory.
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Jupyter Notebook
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Last pushed
Mar 15, 2023
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