Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees holds a master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a master's degreeand in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli".He has over 15 20 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer ITC courses (about 15 20 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control. He was recently included in the world's top 2% scientists list by Stanford University.
Giuseppe Ciaburro - ebooki
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Unlock the power of MATLAB for machine learning with this comprehensive guide. From data handling to advanced techniques like deep learning, NLP, and anomaly detection, gain practical skills applicable across a range of AI applications.
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MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
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Hands-On Simulation Modeling with Python, Second Edition teaches you how to leverage Python to develop simulation models and use various Python packages. The book will help you explore various numerical simulation algorithms and concepts, such as the Markov Decision Process, Monte Carlo methods, and bootstrapping techniques.
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Hands-On Simulation Modeling with Python. Develop simulation models for improved efficiency and precision in the decision-making process - Second Edition Hands-On Simulation Modeling with Python. Develop simulation models for improved efficiency and precision in the decision-making process - Second Edition
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Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems.
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Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
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With this book, you will learn how to perform various machine learning tasks in different environments. You’ll use a wide variety of machine learning algorithms using Python to solve real-world problems. By the end of the book, you will learn to implement most used machine learning algorithms using complex datasets and optimized techniques.
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Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition
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Keras is a deep learning library that enables the fast, efficient training of deep learning models. The book begins with setting up the environment, training various types of models in the domain of deep learning and reinforcement learning. The projects are exciting and are real-world market demanding projects which take you from simple to complex
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Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
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Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras
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Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents
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Azure Data Factory (ADF) is a Microsoft Azure PaaS solution which supports data movement between many on premises and cloud data sources. This book covers custom tailored tutorials to help you develop , maintain and troubleshoot data movement processes and environments using Azure Data Factory V2 and SQL Server Integration Services 2017
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Hands-On Data Warehousing with Azure Data Factory. ETL techniques to load and transform data from various sources, both on-premises and on cloud Hands-On Data Warehousing with Azure Data Factory. ETL techniques to load and transform data from various sources, both on-premises and on cloud
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In this book, you will learn how to create powerful machine learning based applications for a wide variety of problems leveraging different data services from the Google Cloud Platform. Finally, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
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Hands-On Machine Learning on Google Cloud Platform. Implementing smart and efficient analytics using Cloud ML Engine Hands-On Machine Learning on Google Cloud Platform. Implementing smart and efficient analytics using Cloud ML Engine
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Regression analysis is a statistical process which enables prediction of relationships between variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the
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Regression Analysis with R. Design and develop statistical nodes to identify unique relationships within data at scale Regression Analysis with R. Design and develop statistical nodes to identify unique relationships within data at scale
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The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.
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Neural Networks with R. Build smart systems by implementing popular deep learning models in R Neural Networks with R. Build smart systems by implementing popular deep learning models in R
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MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will build a foundation for machine learning using MATLAB for beginners. It will also help you learn regression, clustering, classification, predictive analytics, artificial neural networks, and more with MATLAB.
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MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks