AI-Artificial Intelligence is any technique that aims to allow computers to mimic human behavior, including machine learning, natural language processing (NLP), language synthesis, computer vision, robotics, sensor analysis, optimization and simulation.
In this context, Machine Learning (ML) is a subset of AI techniques that allow computer systems to learn from previous experience (ie from data observations) and improve their behavior for a particular task. ML techniques include Support Vector Machines (SVM), decision trees, Bayes learning, k-means clustering, association rules, regression, neural networks and more.
Therefore, Machine Learning, Deep Learning, Deep Neural Network using R. Theory and applications is designed to support all those who want to know more or less analytical aspects of machine learning and deep learning, in the context of deep neural networks, from the perspective of the application of neural networks.
By going through the content of this book, students will be able to successfully complete the exams based on specific elements presented, young PhD students will find enough useful elements in preparing their theses containing aspects of maximum novelty, and all others interested in this material will find enough information to reach the intellectual side of each of them.
Consequently, the work Machine Learning, Deep Learning, Deep Neural Network using R. Theory and applications is recommended both for students who have in the curriculum disciplines represented by the keywords in the title of this paper, as well as for researchers and those interested in various aspects related to this field.
For those interested in knowing other aspects of Machine Learning, Deep Learning, Deep Neural Network using the R environment, the presented bibliography represents a good guide in this regard.