Technical Fridays | Blog
Personal notes on machine learning, deep learning, and software engineering — what's Technical Fridays?
- Personal
- Data Science
- Machine Learning
- R
- Python
- Algorithms
- Cryptography
- Mathematics
- Visualization
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Speech Recognition
- PyTorch
- LLM
- CUDA
- Agentic AI
-
Data ScienceGradient boosted trees: Better than random forest?
Comparing gradient boosted trees and random forests, their differences in training strategy, tuning requirements, and when to prefer each.
-
Data ScienceLinear algebra: The essence behind deep learning
How linear algebra underpins deep learning from score functions and weight matrices to image classification with neural networks.
-
Data ScienceData Mining: Knowledge discovery in databases
An overview of the KDD (Knowledge Discovery in Databases) process and how data mining, machine learning, and data science relate to each other.
-
Data ScienceAnscombe's Quartet
Anscombe's quartet illustrates why visualizing data matters, four datasets with nearly identical statistics but completely different distributions.
-
Data Science
The Curse of Dimensionality
Why increasing the number of features degrades kNN performance, the curse of dimensionality explained intuitively and mathematically.
-
Data Science
Dealing with categorical data
Techniques for encoding categorical variables in machine learning models, including dummy variables and one-hot encoding.
-
Data Science
Regularization
How regularization techniques, L1 (Lasso) and L2 (Ridge), add penalty terms to the loss function to combat overfitting in linear models.
-
MathematicsSome Prime Thoughts
Exploring prime numbers, primality testing in Python, the Fundamental Theorem of Arithmetic, and their role in cryptography.
-
Data ScienceEvaluation metrics for classification and False positives
A guide to classification evaluation metrics: confusion matrix, precision, recall, and the significance of false positives.
-
Data ScienceSimplicity doesn't imply accuracy
Examining Occam's razor in machine learning, why simpler models aren't always more accurate and how complexity relates to overfitting.