Parse GGUF, dequantize Q4_0 and Q8_0 tensors, and load Llama weights into numpy. A hands-on guide to a side project that demonstrates real systems engineering on a CV.
Build flash attention from scratch in pure NumPy using tiling and the online softmax trick, then extend it toward production. A deep, runnable project that signals real systems engineering to hiring managers.
A working engineer’s guide to implementing Flash Attention from the ground up using tiling and the online softmax trick — runnable NumPy, end-to-end tests, and a roadmap to senior-level extensions.
Introduction In today’s data‑driven landscape, the ability to move data efficiently from raw sources to sophisticated AI models is a competitive advantage. A data pipeline is the connective tissue that stitches together ingestion, cleaning, transformation, feature engineering, model training, and deployment. While many practitioners start with simple NumPy arrays for prototyping, production‑grade pipelines demand a richer toolbox: Pandas for tabular manipulation, Dask for parallelism, Apache Airflow or Prefect for orchestration, and deep‑learning frameworks such as TensorFlow or PyTorch for model training. ...
NumPy, short for Numerical Python, is the foundational library for scientific computing in Python, providing efficient multidimensional arrays and a vast collection of mathematical functions.[1][2][5] This comprehensive guide takes you from absolute beginner to advanced NumPy hero, complete with code examples, practical tips, and curated resource links. Whether you’re a data scientist, machine learning engineer, or just starting with Python, mastering NumPy will supercharge your numerical workflows. Let’s dive in! ...