Understanding Tensor Data Structures with Go
Analysis
This article from Zenn ML details the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning, using the Go programming language. The author prioritizes understanding the concept by starting with a simple implementation and then iteratively improving it based on existing libraries like NumPy. The article focuses on the data structure of tensors and optimization techniques learned during the process. It also mentions a related article on automatic differentiation. The approach emphasizes a practical, hands-on understanding of tensors, starting from basic concepts and progressing to more efficient implementations.
Key Takeaways
- •The article focuses on implementing tensors in Go.
- •The author prioritizes understanding over initial performance.
- •The implementation is improved by referencing existing libraries like NumPy.
“The article introduces the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning.”