Machine Learning
The Future of ML: What Keeps Me Excited
Despite the hype cycles and the challenges, I’m more excited about ML than ever. The progress in the last year alone is staggering. We’re seeing models…
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Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Open Source Contribution: How to Get Started
One of the best ways to learn is to contribute to open source. But it’s intimidating. Where do you start? I started by fixing documentation typos. Seriou…
Machine Learning
Imposter Syndrome in Tech: You Belong Here
I’ve been working in ML for a few years now, and I still get imposter syndrome. I look at papers on ArXiv and feel like I know nothing. I scroll through…
Machine Learning
The Math You Actually Need: Calculus, Linear Algebra, and Stats
When I started, I was terrified of the math. Do you need to be a mathematician to do ML? No. But you do need intuition. You don’t need to solve integrals…
Machine Learning
Building a Portfolio: Projects Over Certificates
I’ve seen people with dozens of certificates who can’t solve a real-world problem. And I’ve seen self-taught people with amazing GitHub portfolios la…
Machine Learning
The High Cost of Training: Energy and Environment
We often ignore the environmental cost of training large models. Some of these models consume as much energy as a car over its lifetime. I’ve become more…
Machine Learning
Transformers: The Architecture That Changed Everything
Before Transformers, we had RNNs and LSTMs, which were slow and had memory issues. Then the 'Attention is All You Need' paper dropped, and everything chang…
Machine Learning
The Rise of Generative AI: More Than Just ChatGPT
Generative AI has exploded, and it’s not just about text. I’ve been experimenting with Stable Diffusion and similar models. It’s mind-blowing to gene…
Machine Learning
Graph Neural Networks: The Next Frontier
I recently got into Graph Neural Networks (GNNs), and it’s fascinating. Traditional ML assumes data points are independent. But what about social network…