Breaking the Jargons #Issue 11

A Roundup of My Recent Articles on AI and Data Science

Over the past few months I’ve been writing a lot more regularly. If you’ve recently subscribed or haven’t been following along on Medium, here are a few articles you may have missed.

1. Can This Model Run on My Phone?

As more AI applications move onto personal devices, one of the first questions becomes whether a model can actually run on the hardware people already own. In this article, I explore three apps that make it easy to experiment with AI models on real devices.

Link: https://pandeyparul.medium.com/can-this-model-run-on-my-phone-f549353695b8

2. Three ways in which Ollama makes trying new models much easier now

New open weights models are being released at a pace where the real bottleneck is often not access but whether your machine can actually run them or not. In this article, I explore three ways Ollama makes it easier to quickly experiment with new models before committing to a download.

Link: https://pandeyparul.medium.com/three-ways-in-which-ollama-makes-trying-new-models-much-easier-now-a089d0ec18f7

3. Reading Research Papers in the age of LLMs

Keeping up with AI research is harder than ever. In this article, I share how my paper-reading workflow has changed over the years and how I combine manual reading with AI tools to stay on top of new research.

Link: https://pandeyparul.medium.com/reading-research-papers-in-the-age-of-llms-63b127d2ad38

4. Federated Learning, Part 1: The Basics of Training Models Where the Data Lives

What if models could learn from data without moving it to a central server? This article introduces the core ideas behind federated learning and why it matters for privacy-sensitive applications.

Link: https://pandeyparul.medium.com/federated-learning-part-1-the-basics-of-training-models-where-the-data-lives-a6d40469c898

5. Federated Learning Part 2: Implementation with the Flower Framework

After covering the fundamentals of federated learning, this article walks through a complete implementation using the Flower framework, including local training, model aggregation, and federated rounds.

Link: https://pandeyparul.medium.com/federated-learning-part-2-implementation-with-the-flower-framework-c73e061b37d3