Silpa - ML Digest - Page 8 of 9

Predictive vs. Generative Models: A Quick Guide

In ML, predictive and generative models are two fundamental approaches to building ML models. While both have their unique strengths […]

The embedding layer in LLM is a critical component that maps discrete input tokens (words, subwords, or characters) into continuous

Transformers have revolutionized the field of NLP. Central to their success is the attention mechanism, which has significantly improved how

Neural networks have revolutionized various fields, from image and speech recognition to natural language processing. The primary goal of training

The loss function quantifies the difference between the predicted output by the model and the actual output (or label) in

Neural networks are inspired by the human brain, where neurons communicate through synapses. Just as biological neurons are activated when

SmolLM2 is a family of compact language models, available in three sizes: 135M, 360M, and 1.7B parameters. These models are

Qwen2.5 marks a significant milestone in the evolution of open-source language models, building upon the foundation established by its predecessor,

While data scientists and machine learning engineers often collaborate closely and their work may overlap, there are distinct differences in

Key Features of the Dataset The dataset used to train Smollm2 is a critical component of its success. Here are

The emergence of small language models (SLMs) is poised to revolutionize the field of artificial intelligence. These models, exemplified by

Testing machine learning code is essential for ensuring the quality and performance of your models. However, it can be challenging