Data Science And Machine Learning Series Deep Learning Facts Frameworks And Functionality
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Data Science and Machine Learning Series: Deep Learning: Facts, Frameworks, and Functionality
Explore deep learning and how it applies to data science and compares to traditional machine learning. See how deep learning works in terms of both architecture and design, and learn about different frameworks including Apache MXNet, PyTorch, and TensorFlow. Related concepts are covered including Artificial Neural Networks (ANNs), Multi-Layer Perceptrons (MLPs), and Natural Language Processing (NLP). Programming languages including Python and Julia are discussed. Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos.
Machine Learning for Data Science Handbook
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Artificial Intelligence, Big Data and Data Science in Statistics
This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.