pphuc25/deep-learning-argorithms-from-sratch
⌨️ Implementations/tutorials of deep learning papers with side-by-side notes
This resource provides clear, step-by-step implementations of various deep learning algorithms and neural network architectures. It takes the mathematical concepts from deep learning papers and translates them into working code, offering detailed explanations alongside. It's designed for students, researchers, or anyone seeking to understand the inner workings of AI models.
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Use this if you are an AI/ML student or researcher who wants to gain a deeper, foundational understanding of how deep learning algorithms like Transformers, GANs, or Diffusion Models are constructed from first principles.
Not ideal if you are a practitioner looking to build production-ready deep learning applications or need highly optimized, state-of-the-art model implementations for real-world tasks.
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Python
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MIT
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Last pushed
Jul 23, 2023
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