Building a Trustworthy LLM-as-a-Judge: A Field Guide from the Trenches
Less prompting, more statistics
Posts
Visualizing Context with Google's Universal Sentence Encoder and GraphDB
An interactive exploration of how Google’s Universal Sentence Encoder captures semantic context, visualized using graph databases. Read the full article on Medium → Originally published on Medium’s Data Science publication. This is one of my favorite articles showcasing the power of semantic embeddings through visual exploration.
DSPy in Action: Improving Optimizers Through HardNegatives
Exploring how to improve DSPy optimizers using hard negative examples to enhance model performance. Read the full article on Medium → Originally published on Medium’s Data Science publication.
Neural Structured Learning
An exploration of neural structured learning techniques and their applications in building more robust machine learning models. Read the full article on Medium → Originally published on Medium.
A Non-Technical Introduction to Reinforcement Learning (Part 1)
A beginner-friendly introduction to reinforcement learning concepts, explained without heavy mathematical jargon. Read the full article on Medium → Originally published on Medium.
Custom Datasets in PyTorch (Part 2): Text & Machine Translation
Learn how to create custom datasets in PyTorch for text and machine translation tasks with practical examples. Read the full article on Medium → Originally published on Medium’s Data Science publication.
Custom Dataset in PyTorch (Part 1): Images
A comprehensive guide to creating custom image datasets in PyTorch, covering data loading, transformations, and best practices. Read the full article on Medium → Originally published on Medium’s Data Science publication.
Solving Mathematical Word Problems Through Chain-of-Thought Prompting
Deep dive into using chain-of-thought prompting techniques to solve complex mathematical word problems, with practical examples and insights. Read the full article on Medium → Originally published on Medium.