rajlm10/D2L-Torch
Learning PyTorch through the D2L book. A series of notebooks for the same
This project provides a series of interactive Python notebooks to help you translate your existing deep learning knowledge from other frameworks, like TensorFlow, into PyTorch. It takes the concepts and examples from the "Dive into Deep Learning" textbook and re-implements them with PyTorch code. Data scientists and machine learning engineers who are familiar with deep learning fundamentals but new to PyTorch will find these notebooks useful.
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Use this if you are a machine learning practitioner who already understands deep learning concepts and wants to quickly get up to speed with PyTorch's syntax and structure.
Not ideal if you are an absolute beginner to deep learning, as this resource assumes prior knowledge of neural networks and their underlying principles.
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Jupyter Notebook
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Last pushed
Jun 30, 2022
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