We have seen a new era of agentic IDEs like Windsurf and Cursor AI. Source link

“I’m feeling blue today” versus “I painted the fence blue. Source link

This article is divided into three parts; they are: • Full Transformer Models: Encoder-Decoder Architecture • Encoder-Only Models • Decoder-Only Models The original transformer architecture, introduced in “Attention is All You Need,” combines an encoder and decoder specifically designed for sequence-to-sequence (seq2seq) tasks like machine translation. Source link

Learning machine learning can be challenging. Source link

In machine learning model development, feature engineering plays a crucial role since real-world data often comes with noise, missing values, skewed distributions, and even inconsistent formats. Source link

Machine learning model development often feels like navigating a maze, exciting but filled with twists, dead ends, and time sinks. Source link

This post is divided into five parts; they are: • Naive Tokenization • Stemming and Lemmatization • Byte-Pair Encoding (BPE) • WordPiece • SentencePiece and Unigram The simplest form of tokenization splits text into tokens based on whitespace. Source link

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by reducing the numerical precision of the model’s parameters (weights) — usually from 32-bit floating-point to lower representations like 8-bit integers. Source link

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost of interpretability. Source link