Technical Fridays | Blog
Personal notes on machine learning, deep learning, and software engineering — what's Technical Fridays?
- Personal
- Data Science
- Machine Learning
- Algorithms
- Cryptography
- Mathematics
- Visualization
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Speech Recognition
- CUDA
- LLM
- Agentic AI
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Data ScienceScaling vs Normalization
The difference between feature scaling (min-max) and normalization (standardization), and when to apply each in machine learning pipelines.
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Data ScienceEnsembling is the key
An overview of ensemble learning methods: bagging, random forest, boosting, and stacking, and why combining models often outperforms any single algorithm.
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Data Science
Computational graphs: Backpropagation
Backpropagation explained via computational graphs, a local, chain-rule-based method for computing gradients efficiently in neural networks.
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Data ScienceGradient descent: The core of neural networks
How gradient descent works to optimize neural network weights by following the steepest direction of the loss function.
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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.
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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.
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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.
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Data ScienceAnscombe's Quartet
Anscombe's quartet illustrates why visualizing data matters, four datasets with nearly identical statistics but completely different distributions.
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Data Science
The Curse of Dimensionality
Why increasing the number of features degrades kNN performance, the curse of dimensionality explained intuitively and mathematically.
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Data Science
Dealing with categorical data
Techniques for encoding categorical variables in machine learning models, including dummy variables and one-hot encoding.