Key Strategies for Building Effective Machine Learning Systems
Amelia Nguyen13 Jun 2026
A strategic approach to selecting the right model, better features and evaluation.
Benchmarking Machine Learning Approach for Real-World Fuel Consumption Prediction in ICEVs
Amelia Nguyen11 Jun 2026
Accurate prediction of fuel consumption in internal combustion engine vehicles (ICEVs) from real-world telematics data.
Orthogonalization in Machine Learning
Amelia Nguyen06 Apr 2026
Orthogonalization is the principle of separating concerns in our ML development process, ensuring that each “knob” we tune affects only one aspect of performance, without unintended side effects on ot
Improving Deep Neural Learning Networks (Part 3): Hyperparameter Tuning and Batch Normalization
Amelia Nguyen30 Mar 2026
Getting the algorithm right is half the battle, knowing how to tune it, normalize it, and deploy it is what separates research code from production systems.
Improving Deep Neural Learning Networks (Part 2): Optimization Algorithms
Amelia Nguyen26 Mar 2026
Gradient descent is just the starting point — the real question is how fast and how reliably you can reach a good minimum.
Improving Deep Neural Learning Networks (Part 1): Practical Approaches and Applications to LLMs
Amelia Nguyen21 Mar 2026
From foundational Deep Learning training techniques to the algorithms powering modern Agentic AI. Part 1 of 4.
Why Should We Pay Attention To Reproducibility In Analysis Pipelines?
Amelia Nguyen30 Sep 2024
A practical guide to reproducible analysis pipeline.
Extract data from Google Datastore API with Python and Jupyter Notebook
Amelia Nguyen25 Sep 2024
I had a project that required a lot of work with Google Datastore. However, there is surprisingly few instructional materials for this part. So I hope my article could make the job easier!
Spotify Analysis: How Music Taste Changes Between Then & Now
Amelia Nguyen20 Aug 2021
This is a blog post for my first data analysis project: Spotify EDA, and also my first post featured on CodeX publication.
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