Introduction Artificial Intelligence (AI) is a form of technology that enables machines to perform tasks that would normally require human intelligence and skills. It has been used for many years to forecast weather, find the most efficient route home, predict trends in the stock market, and much more. But recently a…

Hold your large language models accountable by learning how to “explain” the predictions — Introduction Just like a coin, explainability in AI has two faces — one it shows to the developers (who actually build the models) and the other to the users (the end customers). The former face (IE i.e. intrinsic explainability) is a technical indicator to the builder that explains the working of…

The single place to get daily updates on the latest in the field of Artificial Intelligence 🧠 and Machine Learning 🤖 — ➡️ What is this all about? 🤔 Let’s start the article with one question — How do you keep yourself updated with the latest happenings in AI or ML? (really think about it for a second before moving forward) I have asked the same question to a lot of AI/ML practitioners, and a quick summarization of their…

Hugging Face Spaces is an open source app repository, that lets you create, host and demo your AI/ML applications for free! — Introduction Showing off your AI skills has never been easier! Just a couple of years back, if a Data Scientist had an idea, (s)he had to work on it from scratch, hustle to make it accurate, and then get the project hosted on the cloud for some price. A project needed…

A consolidation of notes that briefly but gently introduces Knowledge graphs and shines a light on several practical aspects. — Table of Contents Introduction - What is a Knowledge Graph (KG)? - Why KG? - How to use KG? KG in practice - Open source KGs - Creating custom KG - KG Ontology - Hosting KG (database) - Query facts from KG Introduction In this section, we will introduce KG by asking some simple…

Revisiting some of the best practices in Python by touching points from project structuring to code formatting to some good old code management tips — This article is a chapter taken from my ongoing (and still only partially complete) book on Data science titled, “A Lazy Guide to Data Science”. To read similar articles, refer to my blog or my medium page. Don't forget to say hi on LinkedIn and/or Twitter. 🖖

Briefly theoretical and mostly practical examples of creating psychedelics images using text prompts — Introduction Over the past couple of years, there has been a lot of research and developments into creating AI models that can generate images from a given text prompt. This could be thought of as a personal artist, who tries to create an artwork by following the very words of your…

Going through the basics of massive language models, we learn about the different open-source models and then compare them by fine-tuning each one of them for the sentiment detection task. — The code used in this article can be found here — GPT and T5. To read more about text generation models, see this. For more such articles visit my website or have a look at my latest short book on Data science. You can also connect with me on LinkedIn.

Hang on for a ride as we learn to use BERT for your NLP task, and later spice it up by applying some tricks which could significantly increase the overall performance. — The code used in this article can be found here. For more such articles visit my website or have a look at my latest short book on Data science. You can also connect with me on LinkedIn. Introduction Transformers has taken the NLP world by storm. Recent research has shown Transformers…