Building Machine Learning and Deep Learning Models on Google Cloud Platform, Ekaba Bisong
Автор: Perrotta Paolo Название: Programming Machine Learning: From Zero to Deep Learning ISBN: 1680506609 ISBN-13(EAN): 9781680506600 Издательство: Wiley Рейтинг: Цена: 6098.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they`re easier to understand, and build your confidence by getting your hands dirty.
Описание: This book features papers on implementing intelligent tools for building a scientific information platform. It covers architecture of scientific information platforms, semantic clustering, ontology-based systems and multimedia data processing.
Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Pierluigi Riti Название: Pro DevOps with Google Cloud Platform ISBN: 1484238966 ISBN-13(EAN): 9781484238967 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Use DevOps principles with Google Cloud Platform (GCP) to develop applications and services. This book builds chapter by chapter to a complete real-life scenario, explaining how to build, monitor, and maintain a complete application using DevOps in practice.
Starting with core DevOps concepts, continuous integration, and continuous delivery, you’ll cover common tools including Jenkins, Docker, and Kubernetes in the context of a real microservices application to deploy in the cloud. You will also create a monitor for your cloud and see how to use its data to prevent errors and improve the stability of the system. By the end of Pro DevOps with Google Cloud Platform, you will be able to deploy, maintain, and monitor a real application with GCP.
What You Will Learn
Build and deploy applications and services using DevOps on Google Cloud Platform Maintain a complete continuous integration (CI) and continuous delivery (CD) pipelineUse containerization with Docker and KubernetesCarry out CD with GCP and JenkinsCreate microservices with Jenkins, Docker, and KubernetesMonitor your newly deployed application and its deployment and performanceSet up security and manage your network with GCP
Who This Book Is For
Developers and software architects who want to implement DevOps in practice. Some prior programming experience is recommended as well as a basic knowledge of a Linux command-line environment.
Описание: This book features papers on implementing intelligent tools for building a scientific information platform. It covers architecture of scientific information platforms, semantic clustering, ontology-based systems and multimedia data processing.
Автор: Robert Bembenik; Lukasz Skonieczny; Henryk Rybi?sk Название: Intelligent Tools for Building a Scientific Information Platform ISBN: 3642442285 ISBN-13(EAN): 9783642442285 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In an economic environment full of turmoil, the more versatile our explanatory models the better. This volume widens our perspective on these matters with innovative research on the application of pretopology and topology to economics and management studies.
Описание: Chapter 1: Introduction to Cognitive Virtual BotChapter Goal: To introduce the basics of Cognitive Virtual Bot 1.1 What is Cognitive Chatbot Chapter 2: Introduction to Google DialogflowChapter Goal: To introduce the basics of Google Dialogflow 2.2 What is Google Dialogflow2.3 Use cases for Google Dialogflow2.4 Bot Frameworks2.5 Building your First Bot using Google Dialogflow Chapter 3: Advanced Concepts of Google DialogflowChapter Goal: Details how to build a chatbot with Google Dialogflow3.1 Input context and output context3.2 Follow up intents3.3 Multiple responses3.4 Contextual entities3.5 Handling combination of intents and entities3.6 Event creation3.7 Enable fulfillment - webhook and inline editor3.8 Slots 3.9 Handling intent conflicts3.10 Showcasing the solutions in various formats like Text, HTML and to integrations such as Google Assistant3.11 Multi-lingual chatbots3.12 Prebuilt agents Chapter 4: Use cases for Cognitive Chatbots using Google DialogflowChapter Goal: Provide different use cases and integrations for Cognitive Chatbots using Google Dialogflow.4.1 Chatbot personality via webhook4.2 Simple and complex dialogflow design for travel use case4.3 Integration with Google weather API.4.4 Additional integrations4.5 Intent Identifications - audio, speech responses and sentiment analysis4.6 Integrate Google Dialogflow with other services to enhance the conversational flow and search Chapter 5: Researches in field of Cognitive Virtual ChatbotsChapter Goal: Provides an introduction to the new researches in the areas of Cognitive Virtual Chatbots5.1 Cognitive Virtual Chatbots - research
Автор: Zhang Название: Toward Deep Neural Networks ISBN: 1138387037 ISBN-13(EAN): 9781138387034 Издательство: Taylor&Francis Рейтинг: Цена: 19140.00 р. Наличие на складе: Поставка под заказ.
Описание: This book introduces deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors` 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning· Explanation Methods in Deep Learning· Learning Functional Causal Models with Generative Neural Networks· Learning Interpreatable Rules for Multi-Label Classification· Structuring Neural Networks for More Explainable Predictions· Generating Post Hoc Rationales of Deep Visual Classification Decisions· Ensembling Visual Explanations· Explainable Deep Driving by Visualizing Causal Attention· Interdisciplinary Perspective on Algorithmic Job Candidate Search· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Описание: This book presents machine learning models and algorithms to address big data classification problems. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The third part presents the topics required to understand and select machine learning techniques to classify big data.
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.
Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
In this book, you will:
Use heuristics and design fitness functions.
Build genetic algorithms.
Make nature-inspired swarms with ants, bees and particles.
Create Monte Carlo simulations.
Investigate cellular automata.
Find minima and maxima, using hill climbing and simulated annealing.
Try selection methods, including tournament and roulette wheels.
Learn about heuristics, fitness functions, metrics, and clusters.
Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon.
What You Need:
Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Автор: Gogate & Hollich Название: Theoretical And Computational Models Of Word Learning ISBN: 1466629738 ISBN-13(EAN): 9781466629738 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 25502.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. <br><br><em>Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence</em> strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.
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