Category
Machine Learning
Deep dives into ML algorithms, models, and applications
// 12 articles filed
// FIG. CAT-MACH
PristrenPristrenDeep dives into ML algorithms, models, and applications
// 12 articles filed
ONNX (Open Neural Network Exchange) is the universal model format - export from PyTorch, scikit-learn, or HuggingFace and run 3x faster inference with ONNX Runtime on CPU or GPU.
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
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
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
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
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
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
Time series has seasonality, trend, and temporal dependencies that standard ML ignores. Here is when to use ARIMA vs. LightGBM lag features vs. LSTM — and the critical mistake of random data splits.
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
A full practical walkthrough of training a neural network - data prep, architecture selection, optimizer config, common failure modes, and getting to production.
Mahmudul Haque Qudrati
CEO & ML Engineer
What ML can and cannot do for your product, how to write an ML spec, how to evaluate model readiness, and what PMs consistently get wrong working with data scientists.
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
AI trends, techniques, and real-world implementations
How LLMs work, honest comparisons, and production usage
Every technique that works — with real examples
Claude Code, Cursor, Copilot, open-source tools reviewed honestly
Local LLMs, open models, free AI infrastructure
Fewer tokens, cheaper APIs, local alternatives with real numbers
Benchmarks explained, evaluation frameworks, model testing
LLM SEO, AI SEO, Google AI Overviews, developer marketing
iOS, Android, and cross-platform mobile app development
Modern web technologies, frameworks, and best practices
Data analysis, visualization, and engineering insights
Autonomous agents, LLM applications, and intelligent systems