Designing an AI system often feels like choosing how to travel from Point A to Point B. The destination is fixed (your business outcome), but you can walk, drive, or fly to get there. This article is a practical compass to help you decide when to use rules, traditional ML, or Generative AI – and how to justify that choice.
Picking the Right AI Approach: Choosing Rules, ML, and GenAI Read More »
For Large Language Models (LLMs), inference speed and efficiency are paramount. One of the most critical optimizations for speeding up text generation is KV-Caching (Key-Value Caching).
Understanding KV Caching: The Key To Efficient LLM Inference Read More »
Introduction: The Quest to Understand Language Imagine a machine that could read, understand, and write text just like a human.
How Language Model Architectures Have Evolved Over Time Read More »
Think of a bookshelf versus a long hallway: absolute positional embeddings (APE) assign each token a fixed “slot” on the shelf, while relative positional embeddings (RPE) care only about the distance between tokens — like how far two people stand in a hallway. This article first builds intuition with simple analogies and visual descriptions, then dives into the math: deriving sinusoidal APE, showing how sin–cos interactions yield purely relative terms, and explaining how RPE is injected into attention (including T5-style relative bias). Practical PyTorch examples are provided so the reader can implement APE and RPE, understand their trade‑offs (simplicity and extrapolation vs. relational power), and choose the right approach for real-world sequence tasks.
A Guide to Positional Embeddings: Absolute (APE) vs. Relative (RPE) Read More »
Gradient Boosting is more than just another algorithm; it is a fundamental concept that combines several key ideas in machine learning: the wisdom of ensembles, the precision of gradient descent, and the power of iterative improvement. By building a model that learns from its mistakes in a structured, mathematically-grounded way, it has rightfully earned its place as one of the most effective and versatile tools in a data scientist’s toolkit.
Gradient Boosting: Building Powerful Models by Correcting Mistakes Read More »
FastText is a testament to the power of simple ideas. By treating words as compositions of their parts, it elegantly solves the out-of-vocabulary problem and provides a robust way to represent language. Its speed and efficiency, for both embedding generation and classification, make it a go-to tool for NLP practitioners.
What is FastText? Quick, Efficient Word Embeddings and Text Models Read More »
This guide provides a comprehensive overview of the machine learning (ML) project lifecycle, designed to align stakeholder expectations with the realities of ML development. Key takeaways include:
Iterative, Not Linear: ML projects are cyclical and involve continuous refinement. Early stages are often revisited as the project evolves.
Data is Foundational: A significant portion of project effort, typically 60-70%, is dedicated to data collection, cleaning, and feature engineering. The quality of the data directly determines the success of the model.
Early Feasibility is Crucial: A preliminary study can de-risk projects by validating the approach and identifying data gaps before major resource commitment.
Success is a Partnership: Clear communication, defined business metrics, and stakeholder involvement at each stage are critical for achieving project goals.
A Stakeholder’s Guide to the Machine Learning Project Lifecycle Read More »
Large deep learning models are powerful but often too bulky and slow for real-world deployment. Their size, computational demands, and
Imagine you are a master artist, renowned for creating breathtaking paintings with an infinite palette of colors. Your paintings are
Quantization-Aware Training: The Best of Both Worlds Read More »
Random forest is a powerful ensemble learning algorithm used for both classification and regression tasks. It operates by constructing multiple
The Complete Guide to Random Forest: Building, Tuning, and Interpreting Results Read More »
The variance of a Random Forest (RF) is a critical measure of its stability and generalization performance. While individual decision
How Tree Correlation Impacts Random Forest Variance: A Deep Dive Read More »
Imagine a personal curator who sifts through millions of tweets, understands your evolving interests, and assembles a tailored feed. That
