Implementando Uma Ia Com Dnn Programado Em Python
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Implementando Uma Ia Com Dnn Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-03-15
Implementando Uma Ia Com Dnn 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 2025-03-15 with Computers categories.
A proposta deste código é demonstrar a aplicação de uma Rede Neural Densa (DNN) para a classificação multiclasse do conjunto de dados Iris. O modelo utiliza a biblioteca TensorFlow/Keras e é composto por três camadas ocultas totalmente conectadas (Dense layers) com a função de ativação ReLU, além de uma camada de saída com ativação softmax para classificação em três classes. Após o treinamento, o modelo é avaliado com base na acurácia no conjunto de teste e realiza previsões para algumas amostras, ilustrando o uso de redes neurais profundas para problemas de classificação.
Implementando Uma Ia Com Fnn Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-03-15
Implementando Uma Ia Com Fnn 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 2025-03-15 with Computers categories.
As Redes Neurais Feedforward (FNNs) são um tipo de rede neural artificial em que as informações fluem em uma direção única, da camada de entrada para as camadas ocultas e, finalmente, para a camada de saída, sem conexões cíclicas ou retroalimentação. Esse tipo de rede é amplamente utilizado para tarefas de classificação e regressão, onde a entrada é mapeada para uma saída, sem a necessidade de considerar dependências temporais ou sequenciais, como nas RNNs. No exemplo apresentado, utilizamos o modelo MLPClassifier da biblioteca scikit-learn, que é uma implementação de FNN para realizar a classificação multiclasse no conjunto de dados Iris. O modelo é composto por camadas densas (fully connected), com o número de neurônios e camadas definidas pelo usuário. Após treinar o modelo com os dados de entrada, ele é utilizado para prever as classes dos dados de teste. Essa abordagem é eficiente para problemas de aprendizado supervisionado, como classificação de padrões em imagens, reconhecimento de voz, e outras aplicações em que a relação entre entrada e saída é direta. Esse tipo de rede é simples e eficaz para muitos problemas de aprendizado supervisionado, e sua estrutura torna o treinamento relativamente rápido, especialmente em conjuntos de dados de tamanho moderado, como o Iris.
Implementando Uma Ia Com Cnn Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-03-12
Implementando Uma Ia Com Cnn 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 2025-03-12 with Computers categories.
A proposta deste código é demonstrar a aplicação de uma Rede Neural Convolucional (CNN) para a previsão de dígitos manuscritos a partir de imagens obtidas da internet. Utilizando o modelo treinado com o dataset MNIST, o código recebe uma imagem como entrada, realiza o pré-processamento necessário (como redimensionamento e conversão para escala de cinza), e utiliza a CNN para prever o número presente na imagem. A previsão é realizada combinando o modelo de aprendizado profundo com o pré-processamento adequado, com o objetivo de estimar a classe mais provável de um número manuscrito, sendo essa abordagem útil em sistemas de reconhecimento de caracteres em imagens digitais.
Implementando Uma Ia Com Rnn Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-03-14
Implementando Uma Ia Com Rnn 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 2025-03-14 with Computers categories.
As Redes Neurais Recorrentes (RNNs) são um tipo de modelo de aprendizado profundo especialmente projetado para processar dados sequenciais, como séries temporais, texto e sinais de áudio. Diferentemente das redes neurais tradicionais, que tratam cada entrada de forma independente, as RNNs possuem conexões recorrentes que permitem que informações passadas influenciem as previsões futuras, tornando-as ideais para capturar padrões temporais e dependências de longo prazo nos dados. No exemplo apresentado, utilizamos uma RNN para prever o próximo valor de uma sequência baseada na função seno. O modelo recebe uma sequência de valores como entrada e aprende a estimar o próximo valor esperado, demonstrando a capacidade das RNNs de modelar padrões temporais contínuos. Para isso, o código emprega a biblioteca TensorFlow/Keras, utilizando uma camada SimpleRNN para processar a sequência temporal e uma camada densa na saída para realizar a predição. Esse tipo de abordagem é fundamental para aplicações em previsão de séries temporais, como análise financeira, meteorologia e reconhecimento de padrões em sinais biológicos.
Implementa O Do Modelo Bdi Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-05-11
Implementa O Do Modelo Bdi 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 2025-05-11 with Computers categories.
A inteligência artificial moderna não se limita apenas à execução de comandos ou respostas reativas. Cada vez mais, sistemas autônomos exigem comportamentos deliberativos, capacidade de raciocínio simbólico e tomada de decisões baseadas em objetivos internos. Nesse contexto, surge o modelo BDI (Belief-Desire-Intention) como uma das arquiteturas mais relevantes para o desenvolvimento de agentes inteligentes. Este livro apresenta de forma prática e acessível a implementação do modelo BDI utilizando a linguagem Python, permitindo ao leitor compreender como agentes podem perceber o ambiente (crenças), gerar objetivos (desejos) e tomar decisões coerentes (intenções) com base em suas percepções e metas. Através de exemplos didáticos, o leitor será guiado na criação de agentes que simulam comportamento autônomo, reforçando conceitos fundamentais da inteligência artificial simbólica e da modelagem cognitiva. Destinado a estudantes, pesquisadores e entusiastas de IA, este material serve como um ponto de partida sólido para quem deseja aplicar o paradigma BDI em simulações.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu 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 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
Hands On Neural Networks
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Author : Leonardo De Marchi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30
Hands On Neural Networks written by Leonardo De Marchi 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 2019-05-30 with Computers categories.
Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook Description Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learnLearn how to train a network by using backpropagationDiscover how to load and transform images for use in neural networksStudy how neural networks can be applied to a varied set of applicationsSolve common challenges faced in neural network developmentUnderstand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) networkGet up to speed with advanced and complex deep learning concepts like LSTMs and NLP Explore innovative algorithms like GANs and deep reinforcement learningWho this book is for If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.
Construindo Chatbots Com Python
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Author : Sumit Raj
language : pt-BR
Publisher: Novatec Editora
Release Date : 2019-10-16
Construindo Chatbots Com Python written by Sumit Raj and has been published by Novatec Editora this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-16 with Computers categories.
Construa seu próprio chatbot usando Python e ferramentas open source. Este livro começa com uma introdução aos chatbots na qual você obterá informações vitais sobre sua arquitetura. Em seguida, começará imediatamente a usar o Natural Language Processing (NLP) com a ajuda do Natural Language Toolkit (NLTK) para construir uma plataforma de processamento de linguagem personalizada para seu chatbot. Com essa base inicial, examinará diferentes técnicas de Natural Language Processing para selecionar a que lhe for mais adequada. O próximo estágio é aprender a construir um chatbot usando a plataforma API.ai e definir suas intenções e entidades. No decorrer desse exemplo, você aprenderá a ativar a comunicação com o bot e também examinará os importantes tópicos de sua integração e implantação. O último capítulo de Construindo Chatbots com Python ensinará como construir, treinar e implantar o próprio chatbot. Usando bibliotecas open source e técnicas de machine learning, você aprenderá a prever condições para seu bot e a desenvolver um agente de conversação como uma aplicação web. Para concluir, implantará seu chatbot em seu próprio servidor com a AWS. Neste livro, você: • Conhecerá os aspectos básicos do Natural Language Processing usando Python • Coletará dados e os converterá em dados de treinamento para o chatbot • Construirá seu chatbot a partir do zero como um web app • Integrará seus chatbots ao Facebook, Slack e Telegram • Implantará os chatbots em seu próprio servidor.
Applied Artificial Intelligence
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Author : Wolfgang Beer
language : en
Publisher: Wolfgang Beer
Release Date :
Applied Artificial Intelligence written by Wolfgang Beer and has been published by Wolfgang Beer this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
About This Book Step into the amazing world of Artificial Intelligence and Machine Learning using this compact and easy to understand book. Dive into Neural Networks and Deep Learning and create your own production ready AI models by using TensorFlow and Keras. Work through simple yet insightful examples that will get you up and running with Artificial Intelligence, TensorFlow and Keras in no time. Who This Book Is For This book is for Python developers who want to understand Neural Networks from ground up and build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. What You Will Learn The basic structure and functionality of a Neuron The basic math behind the Neural Network learning process See how to build a simple character recognition model from ground up What classification, regression and clustering is How to use TensorFlow to build production ready models Build a first model with the Keras framework How to predict the survival chance for Titanic passengers How to build a simple book recommender How to detect toxic language with an AI model In Detail Artificial Intelligence became one of the hottest topics in the modern economy, where everything is driven by software, network and data. There exists nearly no startup nor traditional business where Artificial Intelligence is not used extensively across many fields such as search engines, image recognition, robotics or finance. This book gives a ground up, step by step introduction about how a Neural Network is used to learn a given function and to make intelligent data-driven decisions. The book explains how to identify typical use-cases such as classification, regression and clustering in terms of practical and well known use-cases. This book comes with an introduction into the state-of-the-art Google TensorFlow framework that allows developers to roll out their models in production. On top of TensorFlow, the Keras library is used to simplify the design and training of complex deep-learning models. This book comes with multiple examples that show how to apply Artificial Intelligence and Machine Learning models for use-cases such as handwriting recognition, decision making, text analysis and toxic comment identification as well as the use of AI to recommend products to customers.
Deep Learning With Theano
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Author : Christopher Bourez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-31
Deep Learning With Theano written by Christopher Bourez 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 2017-07-31 with Computers categories.
Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. About This Book Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models. Who This Book Is For This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets. Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus. What You Will Learn Get familiar with Theano and deep learning Provide examples in supervised, unsupervised, generative, or reinforcement learning. Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections. Use Theano on real-world computer vision datasets, such as for digit classification and image classification. Extend the use of Theano to natural language processing tasks, for chatbots or machine translation Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment Generate synthetic data that looks real with generative modeling Become familiar with Lasagne and Keras, two frameworks built on top of Theano In Detail This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets. Style and approach It is an easy-to-follow example book that teaches you how to perform fast, efficient computations in Python. Starting with the very basics-NumPy, installing Theano, this book will take you to the smooth journey of implementing Theano for advanced computations for machine learning and deep learning.