Machine Learning Engineer 101
Understanding machine learning and deep learning algorithms can be overwhelming, especially for data scientists diving into frameworks like PyTorch. While tutorials and official documentation provide implementations, they often contain numerous nuances, leaving many learners struggling to grasp the logic behind the algorithms.
This playbook takes a different approach—it focuses on pseudo-code representations of algorithms rather than full-fledged implementations. It strips away unnecessary complexities to highlight the core ideas. Starting from linear regression, we provide step-by-step algorithmic breakdowns, showing key PyTorch functions.
From R to Pytorch
For most statisticians, R is the preferred language for data analysis, statistical modeling, and visualization, thanks to its built-in statistical functions and packages. While R offers simplified vectorized operations, ready-made statistical tools, simplified applications like RMarkdown and Rshiny, it has several limitations in its flexibility and scalability:
- Performance Limitation: single-threaded execution slows down large computations; memory-heavy, struggles with big data without optimizations.
- Inconsistent Syntax & Limited OOP support: syntax across base R, tidyverse; limited data structures.
- Weaker DL Ecosystem and Package Documentation: CRAN packages vary in quality, where the documentation are often poor written, with no direct acess to source codes.
Python provides greater flexibility and scalability but often requires more explicit implementation and is less intuitive. This first part of guide bridges the gap, helping R users understand how to implement statistical models in Python (PyTorch) using pseudo-code and structured steps, ensuring a smooth transition from traditional statistical modeling and work pipeline to more engineer approaches. These examples assist those looking to transition from data science to machine learning engineering by adapting commonly used tools.
