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Word Embeddings in Language Models - MachineLearningMastery.com
Natural language processing (NLP) has long been a fundamental area in computer science. However, its trajectory changed dramatically with the introduction of word embeddings. Before embeddings, NLP relied primarily on rule-based approaches that treated words as discrete tokens. With word embeddings, computers gained the ability to understand language through vector space representations. In this article, you will learn about: How…
Tokenizers in Language Models - MachineLearningMastery.com
Tokenization is a crucial preprocessing step in natural language processing (NLP) that converts raw text into tokens that can be processed by language models. Modern language models use sophisticated tokenization algorithms to handle the complexity of human language. In this article, we will explore common tokenization algorithms used in modern LLMs, their implementation, and how to use them. Let's get…
Encoders and Decoders in Transformer Models - MachineLearningMastery.com
Transformer models have revolutionized natural language processing (NLP) with their powerful architecture. While the original transformer paper introduced a full encoder-decoder model, variations of this architecture have emerged to serve different purposes. In this article, we will explore the different types of transformer models and their applications. Let's get started. Encoders and Decoders in Transformer ModelsPhoto…
A Gentle Introduction to Word Embedding and Text Vectorization - MachineLearningMastery.com
From simple word counting to sophisticated neural networks, text vectorization techniques have transformed how computers understand human language by converting words into mathematical representations that capture meaning and context.
5 Breakthrough Machine Learning Research Papers Already in 2025 - MachineLearningMastery.com
2025 is already a landmark year for machine learning research. Discover five breakthrough papers that are making AI systems faster, more transparent, and easier to understand – from video object tracking to revealing why transformers work so well.
Creating a Secure Machine Learning API with FastAPI and Docker - MachineLearningMastery.com
Learn how to turn your machine learning model into a safe and scalable API using FastAPI and Docker. Get a Handle on Python for Machine Learning! Be More Confident to Code in Python ...from learning the practical Python tricks Discover how in my new Ebook: Python for Machine Learning It provides self-study tutorials with hundreds […]
Advanced Techniques to Build Your RAG System - MachineLearningMastery.com
In the previous post, you learned how to build a simple retrieval-augmented generation (RAG) system. RAG is a powerful approach for enhancing large language models with external knowledge and there are many variations in how to make it work better. In the following, you will see some advanced features and techniques to improve the performance of your RAG system. In…
Building RAG Systems with Transformers - MachineLearningMastery.com
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the capabilities of large language models. By combining the strengths of retrieval systems with generative models, RAG systems can produce more accurate, factual, and contextually relevant responses. This approach is particularly valuable when dealing with domain-specific knowledge or when up-to-date information is required. In this post, you will explore…
Understanding Text Generation Parameters in Transformers - MachineLearningMastery.com
Transformer models are the standard models to use for NLP tasks today. Almost all of the NLP tasks involve generating text but it is not the direct output of the model. You may expect the model to help you generate text that is coherent and contextually relevant. While partially this is related to the quality of the model, the generation…
Applications with Context Vectors - MachineLearningMastery.com
Context vectors are a powerful tool for advanced NLP tasks. They allow you to capture the contextual meaning of words, such as identifying the correct sense of a word in a sentence when it has multiple meanings. In this post, we will explore some example applications of context vectors. Specifically: You will learn how to extract contextual keywords from a…
Generating and Visualizing Context Vectors in Transformers - MachineLearningMastery.com
A context vector is a numerical representation of a word that captures its meaning within a specific context. Unlike traditional word embeddings that assign a single, fixed vector to each word, a context vector for the same word can change depending on the surrounding words in a sentence. Transformers are the tool of choice for generating context vectors today. In…
Example Applications of Text Embedding - MachineLearningMastery.com
Text embeddings have revolutionized natural language processing by providing dense vector representations that capture semantic meaning. In the previous tutorial, you learned how to generate these embeddings using transformer models. In this post, you will learn the advanced applications of text embeddings that go beyond basic tasks like semantic search and document clustering. Specifically, you will learn: How to build…
Using Auto Classes in the Transformers Library - MachineLearningMastery.com
In the transformers library, auto classes are a key design that allows you to use pre-trained models without having to worry about the underlying model architecture. It makes your code more concise and easier to maintain. For example, you can easily switch between different model architectures by just changing the model name; even the code to run the model is…