Three beauties meet in this paper. Words in one language end up internally aligned by meaning. For languages with shared structure, that arrangement is shaped almost the same way across both. A GAN and an SVD recover the rotation between them, strongly for close pairs, weakly for distant ones, with no parallel text.
Less prompting, more statistics
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.
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.
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 beginner-friendly introduction to reinforcement learning concepts, explained without heavy mathematical jargon. Read the full article on Medium → Originally published on Medium.
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.
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.
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.