Daily Dose of Data Science

Daily Dose of Data Science

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Intuitive Guides

Cyclical Feature Engineering
...explained with usage and code.
Nov 22 • Avi Chawla
Momentum in ML, Explained Visually and Intuitively!
(a popular ML interview question)
Nov 7 • Avi Chawla
A Memory-efficient Technique to Train Large Models
...that even LLMs like GPTs and LLaMAs use.
Oct 14 • Avi Chawla
What is (was?) GIL in Python?
...explained with visuals and code.
Oct 13 • Avi Chawla
6 Graph Feature Engineering Techniques
Must-know for building GNNs.
Jul 31 • Avi Chawla
Prompting vs. RAG vs. Finetuning
Which one is best for you?
Jul 18 • Avi Chawla
2 Techniques to Synchronize ML Models in Multi-GPU Training
...explained visually.
Jul 10 • Avi Chawla
DropBlock vs. Dropout for Regularizing CNNs
Addressing a limitation of Dropout when used in CNNs.
Jul 9 • Avi Chawla
Faster Neighbor Search Using Inverted File Index
...actively used in vector DBs.
Jun 5 • Avi Chawla
5 Chunking Strategies For RAG
...explained in a single frame.
May 29 • Avi Chawla
An Animated Guide to KMeans
In 3Blue1Brown style.
May 25 • Avi Chawla
1:41
How to Actually Use Train, Validation and Test Set
Explained with an intuitive analogy.
Apr 30 • Avi Chawla
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