Unlocking the Secrets of ML: A Developer's Journey into Tensor Visualization
Published:Jan 22, 2026 01:23
•1 min read
•r/learnmachinelearning
Analysis
This is a fantastic dive into the core mechanics of machine learning! The author's journey of building an ML runtime from scratch provides an invaluable perspective on how tensors are actually represented and accessed in memory. It's exciting to see this kind of hands-on exploration.
Key Takeaways
- •The article explains how multi-dimensional tensors are flattened into 1D arrays for efficient storage and access in ML.
- •A simple formula is used to calculate the memory offset for accessing individual elements within a tensor.
- •This hands-on approach offers insights into the fundamental operations behind ML libraries.
Reference
“To find the element at [i, j, k], we don't 'index' into a nested structure, we use a simple offset formula: Index=(i ∗ C ∗ D) + (j ∗ D) + k”