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