Chapter 13: Conclusion

We tried to cover a few things, and hope you had fun doing it. Try to add your own layers, re-engineer the autograd engine, or optimize the loops For those looking to dive deeper, the true power and complexity lie in understanding what numpy is doing under the hood(which is beautiful). Fascinating topics to explore include:

• How a matrix is organized inside memory.
• The concepts of strides and offsets that define memory layout.
• The broadcasting rules and reshaping.

The next thing could be creating our own low-level backend, one that is powered by C++ and supports both CPU and GPU execution.

What we did in this book was pretty basic and focused on high-level, simple implementations, without much detailed consideration of underlying memory management, low-level systems programming, or GPU parallel architectures.

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Thank you for reading!