Summarization has become a very helpful way of tackling the issue of data overburden. In my earlier story, I shared how you can create your personal text summarizer using the extractive method — if you have tried that, you may have noticed that, because no new sentences were generated from the original content, at times you may have difficulties understanding the generated … [Read more...] about The Secret Guide To Human-Like Text Summarization
Technology
Is Attention What You Really Need In Transformers?
In recent years there has been an explosion of methods based on self-attention and in particular Transformers, first in the field of Natural Language Processing and recently also in the field of Computer Vision. If you don’t know what Transformers are, or if you want to know more about the mechanism of self-attention, I suggest you have a look at my first article on this … [Read more...] about Is Attention What You Really Need In Transformers?
On DINO, Self-Distillation With No Labels
It has been clear for some time that the Transformers had arrived in the field of computer vision to amaze, but hardly anyone could have imagined such astonishing results from a Vision Transformer in such a short time since their first application. In this article, we discuss one of the most interesting advances in the field of computer vision, DINO, announced a few days … [Read more...] about On DINO, Self-Distillation With No Labels
Graph Convolutional Network Implementation With the PROTEINS Benchmark Dataset
This is part four in a series on graph theory and graph convolutional networks. If you’ve been reading this whole series, you’ve been with me on this entire journey — through discussing what graph theory is and why it matters, what a graph convolutional network even is and how they work, and now we’re here, to the fun part — building our own GCN. If … [Read more...] about Graph Convolutional Network Implementation With the PROTEINS Benchmark Dataset
What Makes Graph Convolutional Networks Work?
This is the third part of a series on graphs and graph theory in machine learning. By now, if you’ve been following this series, you may have learned a bit about graph theory, why we care about graph structured data in data science, and what the heck a “Graph Convolutional Network” is. Now, I’d like to briefly introduce you to what makes these things work. For my … [Read more...] about What Makes Graph Convolutional Networks Work?
Graph Convolutional Networks — Explained
In my last article on graph theory, I briefly introduced my latest topic of interest: Graph Convolutional Networks. If you’re here thinking “what do those words mean?”, you’re in the right place. In this article, we’re going to break this topic down, step by step. Part I: What’s This Graph Thing? If this is the first you’re hearing this ‘graph’ word, I’m sorry, but you … [Read more...] about Graph Convolutional Networks — Explained
Why Graph Theory Is Cooler Than You Thought
What are Graphs? Talk to a scientist in just about any discipline, and ask them the question — based on their discipline — “how does that stuff work?” You’ll likely find that there are systems and networks that you have to consider before you can really understand how any given thing works: whether that’s the human body, a food chain in an ecosystem, a … [Read more...] about Why Graph Theory Is Cooler Than You Thought
Improving Diversity Through Recommendation Systems In Machine Learning and AI
Every day you are being influenced by machine learning and AI recommendation algorithms. What you consume on social media through Facebook, Twitter, Instagram, the personalization you experience when you search, listen, or watch Google, Spotify, YouTube, what you discover using Airbnb and UberEATS, all of these products are powered by machine learning and AI recommender … [Read more...] about Improving Diversity Through Recommendation Systems In Machine Learning and AI
Explainable AI: Application of Shapely Values in Marketing Analytics
Recently, I stumbled upon a white paper, which talked about the latest in AI applications in Marketing Analytics. It specifically talked about the application of XAI (Explainable AI) in marketing mix modelling [white paper]. This caught my attention and I started exploring more about three things: XAI, the current state of marketing analytics, and XAI’s potential applications … [Read more...] about Explainable AI: Application of Shapely Values in Marketing Analytics
How To Get Started With Graph Machine Learning
This blog is a part of my “deep learning update” series and I want to open it up with a question: What have I learned about Graph ML in 2+ months? Nothing? If that was your first thought, no worries, it’s probably true. 😅 (Bad) jokes aside, my “relative knowledge” (the knowledge I “possess” vs the knowledge I’m aware of) is asymptotically … [Read more...] about How To Get Started With Graph Machine Learning