Technical Deep Dives,
& systems logic.
Inference Engine
Building an infernece engine for machine translation transformer model in C++.
6. NLP: Transformers
A complete guide to the transformer architecture, attention mechanisms, and positional encoding.
5. NLP: Attention
A guide on Attention mechanism for seq2seq models.
13. NLP: Positional Encodings
The transformer’s way of remembering who stood where, because attention without order is chaos with confidence.
4. ML: Confusion Matrix, Bias-Variance & Regularization
Model evaluation, bias-variance decomposition, and how regularization techniques constrain complexity.
2. ML: Linear Regression
Mathematical art of drawing a straight line through a cloud of chaos and confidently calling it a prediction.
3. ML: Logistic Regression
Probabilistic power of Logistic Regression: A deep dive into its linear roots and sigmoid derivation.
1. ML: Basic Fundamentals
Building Blocks of Machine Learning
12. NLP: Embeddings
The Geometry of Meaning : Turning meaning into maths.
7. Maths4ML: Matrix Decomposition & SVD
The Art of Mathematical Forgetting: How to throw away 90% of your data without losing the meaning.
6. Maths4ML: Determinants & Eigenvectors
Finding the stillness inside the transformation.
5. Maths4ML: Matrices
Matrices are Machines, Not Just Grids
4. Maths4ML: Kernel Trick
The Kernel Trick - Folding Space. Why struggle to bend the line when you can just fold the space?