research - Robotic Content

A Gentle Introduction to SHAP for Tree-Based Models

by AI Generated Robotic ContentAI/ML Research May 31, 2025Comments are Disabled

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

Using Quantized Models with Ollama for Application Development

by AI Generated Robotic ContentAI/ML Research May 30, 2025Comments are Disabled

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.

Tokenizers in Language Models

by AI Generated Robotic ContentAI/ML Research May 29, 2025Comments are Disabled

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.

10 Python Libraries That Speed Up Model Development

by AI Generated Robotic ContentAI/ML Research May 29, 2025Comments are Disabled

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

Selecting the Right Feature Engineering Strategy: A Decision Tree Approach

by AI Generated Robotic ContentAI/ML Research May 28, 2025Comments are Disabled

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.

Using NotebookLM as Your Machine Learning Study Guide

by AI Generated Robotic ContentAI/ML Research May 27, 2025Comments are Disabled

Learning machine learning can be challenging.

Encoders and Decoders in Transformer Models

by AI Generated Robotic ContentAI/ML Research May 25, 2025Comments are Disabled

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.

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