A Complete Guide to Matrices for Machine Learning with Python
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.
The Beginner’s Guide to Language Models with Python
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.
Understanding the DistilBart Model and ROUGE Metric
This post is in two parts; they are: • Understanding the Encoder-Decoder Architecture • Evaluating the Result of Summarization using ROUGE DistilBart is a “distilled” version of the BART model, a powerful sequence-to-sequence model for natural language generation, translation, and comprehension.
Text Summarization with DistillBart Model
This tutorial is in two parts; they are: • Using DistilBart for Summarization • Improving the Summarization Process Let’s start with a fundamental implementation that demonstrates the key concepts of text summarization with DistilBart: import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM class TextSummarizer: def __init__(self, model_name=”sshleifer/distilbart-cnn-12-6″): “””Initialize the summarizer with a pre-trained model.
Diagnosing and Fixing Overfitting in Machine Learning with Python
Overfitting is one of the most (if not the most!) common problems encountered when building machine learning (ML) models.
Building LLM Applications with Hugging Face Endpoints and FastAPI
FastAPI is a modern and high-performance compliant web framework for building APIs with Python.
10 Python One-Liners That Will Boost Your Data Preparation Workflow
Data preparation is a step within the data project lifecycle where we prepare the raw data for subsequent processes, such as data analysis and machine learning modeling.
Text Generation with GPT-2 Model
This tutorial is in four parts; they are: • The Core Text Generation Implementation • Contrastive Search: What are the Parameters in Text Generation? • Batch Processing and Padding • Tips for Better Generation Results Let’s start with a basic implementation that demonstrates the fundamental concept.
Auto-Completion Style Text Generation with GPT-2 Model
This post is in six parts; they are: • Traditional vs Neural Approaches • Auto-Complete Architecture • Basic Auto-Complete Implementation • Caching and Batched Input When you type in a word in Google’s search bar, such as “machine”, you may find some additional words are suggested, such as “learning,” to make up “machine learning”.
Understanding RAG Part VI: Effective Retrieval Optimization
Be sure to check out the previous articles in this series: •