Explorando Modelos Transformers Programado Em Python
DOWNLOAD
Download Explorando Modelos Transformers Programado Em Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Explorando Modelos Transformers Programado Em Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Explorando Modelos Transformers Programado Em Python
DOWNLOAD
Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-04-28
Explorando Modelos Transformers Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-28 with Computers categories.
A inteligência artificial tem transformado de maneira significativa a forma como interagimos com a tecnologia, e um dos principais responsáveis por esse avanço é o modelo Transformer. Desde a sua introdução, ele revolucionou o campo do Processamento de Linguagem Natural (NLP), permitindo que máquinas compreendam, gerem e analisem texto com uma precisão impressionante. Este livro é uma jornada prática pelo universo dos Transformers, onde exploraremos como esses modelos poderosos podem ser aplicados de maneira eficiente e acessível utilizando a linguagem de programação Python. Através de exemplos práticos, o leitor aprenderá como utilizar a biblioteca Transformers da Hugging Face para implementar tarefas como análise de sentimentos, tradução automática e muito mais. Com uma abordagem didática e direta, este livro oferece o equilíbrio perfeito entre teoria e prática, permitindo que tanto iniciantes quanto profissionais da área se apropriem das tecnologias mais modernas de NLP.
Reconhecimento De Imagens Com Vit Programado Em Python
DOWNLOAD
Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-05-12
Reconhecimento De Imagens Com Vit Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-12 with Computers categories.
Este livro apresenta uma abordagem prática e acessível para o reconhecimento de imagens com o modelo Vision Transformer (ViT), uma arquitetura de aprendizado profundo desenvolvida para aplicar o poder dos transformadores — originalmente projetados para linguagem natural — à área da visão computacional. Através de um exemplo programado em Python, o leitor será guiado na utilização do modelo ViT para classificar imagens reais, inclusive a partir de links da internet, utilizando ferramentas modernas como a biblioteca transformers da Hugging Face. O conteúdo abrange desde o pré-processamento da imagem com o extrator de características até a inferência final com o modelo pré-treinado, permitindo ao leitor compreender como o ViT identifica padrões visuais e associa rótulos a imagens. Esta é uma excelente introdução para estudantes, desenvolvedores e entusiastas que desejam explorar os avanços da inteligência artificial em aplicações visuais do mundo real.
Reconhecmento De Imagens Com Vqa Programado Em Python
DOWNLOAD
Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-04-27
Reconhecmento De Imagens Com Vqa Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-27 with Computers categories.
Este livro aborda o tema do Visual Question Answering (VQA), uma técnica inovadora que combina visão computacional e processamento de linguagem natural para permitir que máquinas compreendam imagens e respondam a perguntas sobre elas. Através de uma abordagem prática, o livro explora como implementar sistemas de VQA usando a linguagem de programação Python, destacando o uso de bibliotecas como Transformers, PyTorch e Hugging Face. Este livro é destinado a desenvolvedores, pesquisadores e entusiastas da inteligência artificial que desejam explorar as possibilidades do VQA, com foco na implementação prática em Python.
Reconhecimento De Imagens Com Vilt Programado Em Python
DOWNLOAD
Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-04-28
Reconhecimento De Imagens Com Vilt Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-28 with Computers categories.
Este livro apresenta de maneira prática como utilizar o modelo Vision-and-Language Transformer (ViLT) para reconhecimento de imagens com apoio textual. Explorando o poder das redes neurais multimodais, mostramos como relacionar imagens a descrições em linguagem natural usando Python e bibliotecas modernas como transformers e torch. Com um exemplo claro e objetivo, o leitor aprenderá como baixar imagens da internet, processá-las juntamente com textos descritivos e avaliar automaticamente a correspondência entre imagem e descrição, aplicando técnicas avançadas de inferência e interpretação de resultados.
Mastering Transformers
DOWNLOAD
Author : Savaş Yıldırım
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-15
Mastering Transformers written by Savaş Yıldırım and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with Computers categories.
Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.
Transformer Models
DOWNLOAD
Author : Jamie Flux
language : en
Publisher: Independently Published
Release Date : 2025-01-18
Transformer Models written by Jamie Flux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-18 with Computers categories.
A Powerful Academic Resource on Transformer-Based Models Immerse yourself in cutting-edge Transformer architectures, where advanced research and practical implementation converge. This comprehensive resource uses full Python code to guide you from foundational concepts to sophisticated real-world applications. Whether you're a researcher seeking rigorous theoretical underpinnings or a professional aiming for state-of-the-art performance across NLP, computer vision, and multi-modal tasks, this text delivers clear explanations, hands-on tutorials, and innovative best practices. Highlights of Featured Algorithms Text Classification with Pre-Trained Models Delve into advanced fine-tuning techniques that boost accuracy across sentiment analysis and topic allocation tasks. Aspect-Based Sentiment Analysis Extract nuanced opinions on specific product or service attributes with specialized attention mechanisms. Vision Transformers for Image Classification Discover how sequence-based patch embeddings enable remarkable object recognition accuracy on complex datasets. Named Entity Recognition Implement robust token-level labelers strengthened by deep contextual embeddings, critical for biomedical or financial text. Time-Series Forecasting Uncover the long-term temporal dependencies in stock data or IoT sensor readings using multi-head self-attention. Graph Transformers for Node Classification Capture intricate relationships in social networks or molecular structures with specialized structural embeddings and graph-based attention. Zero-Shot Classification Classify unseen data on-the-fly by leveraging prompt-based approaches and semantic embeddings learned from extensive pre-training. Packed with step-by-step instructions, well-documented code, and time-tested optimization tips, this resource equips you to push Transformer capabilities to their limits-across both emerging and established domains.