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.
“I’m feeling blue today” versus “I painted the fence blue.
We have seen a new era of agentic IDEs like Windsurf and Cursor AI.
If you’ve been into machine learning for a while, you’ve probably noticed that the same books get recommended over and over again.
In many data analysis processes, including machine learning , data preprocessing is an important stage before further analysis or model training and evaluation.
Machine learning research continues to advance rapidly.
Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in machine learning.
Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of them) get updated.
Python has evolved from a simple scripting language to the backbone of modern data science and machine learning.
Machine learning workflows require several distinct steps — from loading and preparing data to creating and evaluating models.