Machine learning

import functools import pprint import re   import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM     class AdvancedTextSummarizer:     def __init__(self, model_name=“sshleifer/distilbart-cnn-12-6”, quantize=False):         “”“Initialize the advanced summarizer with additional features.           Args:             model_name (str): Name of the pre-trained model to use             quantize (bool): Whether to quantize the model for faster inference         ““”         self.device = “cuda” if … Read more

Statistical Methods for Evaluating LLM PerformanceImage by Author | Ideogram Introduction The large language model (LLM) has become a cornerstone of many AI applications. As businesses increasingly rely on LLM tools for tasks ranging from customer support to content generation, understanding how these models work and ensuring their quality has never been more important. In … Read more

DistilBart is a typical encoder-decoder model for NLP tasks. In this tutorial, you will learn how such a model is constructed and how you can check its architecture so that you can compare it with other models. You will also learn how to use the pretrained DistilBart model to generate summaries and how to control … Read more

The Beginner’s Guide to Language Models with PythonImage by Author | Ideogram Introduction Language models — often known for the acronym LLM for Large Language Models, their large-scale version — fuel powerful AI applications like conversational chatbots, AI assistants, and other intelligent text and content generation apps. This article provides a concise and basic understanding … Read more

Understanding RAG Part VII: Vector Databases and Indexing StrategiesImage by Editor | Midjourney & Canva Be sure to check out the previous articles in this series: Efficiently retrieving knowledge in RAG systems is key to providing accurate and timely responses. Vector databases and indexing strategies play a crucial role in strengthening RAG systems’ performance. This … Read more

A Complete Guide to Matrices for Machine Learning with PythonImage by Author | Ideogram Introduction Matrices are a key concept not only in linear algebra but also with regard to their prominent application and use in machine learning (ML) and data science. In essence, a matrix is a structured approach to represent and manipulate data, … Read more

An excellent way to put their newfound skills could by allowing them to work on Machine Learning Projects for Students with Python which will be quite exiting opportunity for the students across varied domains. This chapter goes deep inside predictive modeling, which is just scratching the surface of what machine learning can do, from predicting … Read more

Introduction to Data Science in Python What is Data Science? Data science helps us understand the vast amount of information around us, from social media and online purchases to weather reports and scientific studies. It provides tools to uncover important insights and patterns in this data.   Data Science Overview Data science combines various fields … Read more

What is data preprocessing in machine learning Data preprocessing in machine learning is the step where we take raw data and clean it up so that it’s easier to work with. Raw data often has missing information, errors, or inconsistencies, making it hard to use directly in machine learning models. Preprocessing fixes these problems by … Read more

What Are Random Forests? Random forests are a type of machine learning algorithm used for decision-making, particularly in classification and regression tasks. They resemble decision trees but are more complex and effective, especially with large datasets. A random forest is essentially a collection of multiple decision trees grouped together. Unlike a single decision tree, which … Read more