[P] MNIST from scratch in Metal (C++)
Summary
This article discusses building a simple 2-layer MNIST MLP using Apple's metal-cpp library, focusing on GPU programming and performance optimization on Apple Silicon.
Why It Matters
Understanding GPU programming is crucial for developers working with machine learning on Apple devices. This article provides insights into optimizing performance through API-level choices, which can significantly impact the efficiency of machine learning models.
Key Takeaways
- The project demonstrates a practical application of GPU programming using Metal.
- API-level choices can significantly affect performance in machine learning tasks.
- The author emphasizes learning through hands-on experience with real-world applications.
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