Classical ML and Deep Learning
A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KAN)
What are KANs, how are they trained, and what makes them so powerful?
Machine Learning
A collection of 62 posts
Engineering Best Practices
Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming
A beginner-friendly guide for curious minds who don't know the internal workings of model.cuda().
Engineering Best Practices
8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science
8 data science lesson I wish I had known earlier.
Classical ML and Deep Learning
15 Ways to Optimize Neural Network Training (With Implementation)
Techniques that help you become a "machine learning engineer" from a "machine learning model developer."
Classical ML and Deep Learning
A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch
Immensely simplify deep learning model building with PyTorch Lightning.
LLM and Fine-tuning
Augmenting LLMs: Fine-Tuning or RAG?
Understanding the tradeoffs between RAG and Fine-tuning, and owning the model vs. using a third-party host.
LLM and Fine-tuning
Implementing LoRA From Scratch for Fine-tuning LLMs
Understanding the challenges of traditional fine-tuning and addressing them with LoRA.
Machine Learning
Mixed Precision Training
Train large deep learning models efficiently.
Engineering Best Practices
Model Compression: A Critical Step Towards Efficient Machine Learning
Four critical ways to reduce model footprint and inference time.
Engineering Best Practices
Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit
Deployment has possibly never been so simple.
Engineering Best Practices
PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.
Eliminating the dependence of PyTorch models on Python.
Engineering Best Practices
Optimize Your ML Development and Operations with MLflow
The guide that every data scientist must read to manage ML experiments like a pro.
Engineering Best Practices
How to Streamline Your Machine Learning Workflow With DVC
The guide that every data scientist must read to manage ML experiments like a pro.
Engineering Best Practices
You Cannot Build Large Data Projects Until You Learn Data Version Control!
The underappreciated, yet critical, skill that most data scientists overlook.
Classical ML and Deep Learning
A Mathematical Deep Dive Into the Curse of Dimensionality
Mathematically understanding the surprising phenomena that arise when dealing with data in high dimensions.
Classical ML and Deep Learning
Why Bagging is So Ridiculously Effective At Variance Reduction?
Diving into the mathematical motivation for using bagging.
Engineering Best Practices
Sklearn Models are Not Deployment Friendly! Supercharge Them With Tensor Computations.
Speed up sklearn model inference up to 50x with GPU support.
Classical ML and Deep Learning
Gaussian Mixture Models (GMMs)
Gaussian Mixture Models: A more robust alternative to KMeans.
Classical ML and Deep Learning
DBSCAN++: The Faster and Scalable Alternative to DBSCAN Clustering
Addressing major limitations of the most popular density-based clustering algorithm — DBSCAN.
Classical ML and Deep Learning
Formulating and Implementing the t-SNE Algorithm From Scratch
The most extensive visual guide to never forget how t-SNE works.
Classical ML and Deep Learning
Bayesian Optimization for Hyperparameter Tuning
The caveats of grid search and random search and how Bayesian optimization addresses them.
Classical ML and Deep Learning
Formulating and Implementing XGBoost From Scratch
An extensive visual guide to never forget how XGBoost works.
Classical ML and Deep Learning
Formulating the Principal Component Analysis (PCA) Algorithm From Scratch
Approaching PCA as an optimization problem.
Classical ML and Deep Learning
You Are Probably Building Inconsistent Classification Models Without Even Realizing
The limitations of always using cross-entropy loss in ordinal datasets.
Classical ML and Deep Learning
Why R-squared is a Flawed Regression Metric?
The lesser-known limitations of the R-squared metric.
Classical ML and Deep Learning
Generalized Linear Models (GLMs): The Supercharged Linear Regression
The limitations of linear regression and how GLMs solve them.
Classical ML and Deep Learning
Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?
What are we missing here?
Classical ML and Deep Learning
Why Do We Use log-loss To Train Logistic Regression?
The origin of log-loss.
Classical ML and Deep Learning
Why Do We Use Sigmoid in Logistic Regression?
The origin of the Sigmoid function and a guide on modeling classification datasets.
Classical ML and Deep Learning
The Probabilistic Origin of Regularization
Where did the regularization term come from?
