Machine Learning
Ethics in Machine Learning: More Than Just an Afterthought
It’s easy to get caught up in the technical coolness of ML and forget about the human impact. I’ve had to sit in meetings where we realized our trainin…
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Machine Learning
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Machine Learning
Machine Learning
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Machine Learning
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Machine Learning
Explainable AI: Why Black Boxes Are Scary
There’s a rising demand for models that can explain themselves. It’s not enough to say 'the model denied the loan'. You need to say why. I’ve been us…
Machine Learning
Version Control for Data and Models: DVC and Git
Git is great for code, but what about your 50GB dataset? Or the 100 different model versions you trained? I learned the hard way when I accidentally overwr…
Machine Learning
Deployment Hell: When Models Meet the Real World
Building a model in a Jupyter notebook is the easy part. Getting it into production? That’s where dreams go to die. I’ve spent months on a model only t…
Machine Learning
The Struggle with Imbalanced Datasets
Imbalanced datasets are the bane of my existence. You have a million examples of one class and only a hundred of the other. The model just gets lazy and pr…
Machine Learning
Model Evaluation: Beyond Just Accuracy
Early on, I would just look at accuracy to see if my model was good. Then I worked on a fraud detection project. If 99% of transactions are normal, a model…
Machine Learning
Reinforcement Learning: The Trial and Error Method
Reinforcement learning (RL) feels the closest to how we actually learn as humans. It’s all about an agent interacting with an environment, getting reward…
Machine Learning
Natural Language Processing: Teaching Machines to Read
I always thought NLP was magic. How can a machine understand sarcasm or context? The reality is, they don't 'understand' in the human sense. They learn pat…
Machine Learning
Computer Vision Basics: How Machines See the World
It’s wild to think that a computer sees an image as just a grid of numbers. When I started working with computer vision, I kept trying to think like a hu…