The Roadmap for Mastering AI-Assisted Coding in 2025
AI-assisted coding was something virtually nobody could even imagine a few years back, but to some extent, it has now become part of many developers’ workflows — be it for generating specific code snippets, debugging existing code, or even orchestrating tasks.
10 Common Misconceptions About Large Language Models
Large language models (LLMs) have rapidly integrated into our daily workflows.
Multi-Agent Systems: The Next Frontier in AI-Driven Cyber Defense
The increasing sophistication of cyber threats calls for a systemic change in the way we defend ourselves against them.
ROC AUC vs Precision-Recall for Imbalanced Data
When building machine learning models to classify imbalanced data — i.
7 Scikit-learn Tricks for Optimized Cross-Validation
Validating machine learning models requires careful testing on unseen data to ensure robust, unbiased estimates of their performance.
A Gentle Introduction to Batch Normalization
Deep neural networks have drastically evolved over the years, overcoming common challenges that arise when training these complex models.
Small Language Models are the Future of Agentic AI
This article provides a summary of and commentary on the recent paper
10 Python One-Liners Every Machine Learning Practitioner Should Know
Developing machine learning systems entails a well-established lifecycle, consisting of a series of stages from data preparation and preprocessing to modeling, validation, deployment to production, and continuous maintenance.
3 Ways to Speed Up and Improve Your XGBoost Models
Extreme gradient boosting ( XGBoost ) is one of the most prominent machine learning techniques used not only for experimentation and analysis but also in deployed predictive solutions in industry.
5 Key Ways LLMs Can Supercharge Your Machine Learning Workflow
Experimenting, fine-tuning, scaling, and more are key aspects that machine learning development workflows thrive on.