Field Notes
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Insights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
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PristrenPristrenInsights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
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109–120 of 528
DevRel is not marketing for developers - it is authentic technical engagement. Here is what it involves, when to invest in it, and how to measure whether it is working.
Mahmudul Haque Qudrati
CEO & ML Engineer
The typical SaaS case study is a press release. A converting case study shows the real before state, what failed first, specific numbers, and an honest implementation story.
Mahmudul Haque Qudrati
CEO & ML Engineer
Rankings drop. Content ages. Here is the systematic playbook for identifying which posts to refresh, how to update them, and when refreshing is the wrong answer.
Mahmudul Haque Qudrati
CEO & ML Engineer
Gradient descent is the engine behind every modern ML model. Here is how it works, why learning rate matters, and when to use Adam over SGD.
Mahmudul Haque Qudrati
CEO & ML Engineer
Supervised learning is the most widely used ML paradigm. Here is exactly how the train-measure-adjust loop works, where labels come from, and when the approach breaks down.
Mahmudul Haque Qudrati
CEO & ML Engineer
Decision trees overfit easily. Random forests fix this by averaging many trees. XGBoost pushes further with gradient boosting. Here is when tree methods beat neural networks.
Mahmudul Haque Qudrati
CEO & ML Engineer
Feature engineering is where most ML project time actually goes. Here is how to do log transforms, one-hot encoding, cyclical encoding, and interaction features that move the needle.
Mahmudul Haque Qudrati
CEO & ML Engineer
Accuracy is misleading on imbalanced datasets. Here is when to use precision, recall, F1, AUC-ROC, MAE, RMSE, and how to choose the right metric for your problem.
Mahmudul Haque Qudrati
CEO & ML Engineer
Transfer learning lets you start from a pretrained model instead of random weights. Here is why it works, when to fine-tune vs. freeze layers, and when it fails.
Mahmudul Haque Qudrati
CEO & ML Engineer
Anomaly detection finds rare events without labeled examples. Here is how Isolation Forest, One-Class SVM, and Autoencoders work -- and why accuracy is the wrong metric.
Mahmudul Haque Qudrati
CEO & ML Engineer
High-dimensional data is hard to work with. PCA, t-SNE, and UMAP each reduce it differently. Here is when to use each and how to avoid the curse of dimensionality.
Mahmudul Haque Qudrati
CEO & ML Engineer
CNNs use convolutions to detect local patterns in images. Pooling downsamples. ResNet residual connections solve vanishing gradients. Here is when to train from scratch vs. use a pretrained model.
Mahmudul Haque Qudrati
CEO & ML Engineer