Category: AI/ML Research

MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.

If you’ve been using large language models like GPT-4 or Claude, you’ve probably wondered how they can write actually usable code, explain complex topics, or even help you debug your morning coffee routine (just kidding!).

This post is divided into three parts; they are: • Interpolation and Extrapolation in Sinusoidal Encodings and RoPE • Interpolation in Learned Encodings • YaRN for Larger Context Window Sinusoidal encodings excel at extrapolation due to their use of continuous functions: $$ begin{aligned} PE(p, 2i) &= sinleft(frac{p}{10000^{2i/d}}right) \ PE(p, 2i+1) &= cosleft(frac{p}{10000^{2i/d}}right) end{aligned} $$ You …

Read more “Interpolation in Positional Encodings and Using YaRN for Larger Context Window”

Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand and explain their predictions.

This post is divided into five parts; they are: • Understanding Positional Encodings • Sinusoidal Positional Encodings • Learned Positional Encodings • Rotary Positional Encodings (RoPE) • Relative Positional Encodings Consider these two sentences: “The fox jumps over the dog” and “The dog jumps over the fox”.

Pandas , NumPy , and Scikit-learn .

Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.

You’ve trained your machine learning model, and it’s performing great on test data.

There’s no doubt that search is one of the most fundamental problems in computing.