Leonardo De Marchi

This Element outlines an overview of popular music made in Brazil, from the nineteenth century to the beginning of the twenty-first century. Initially addressing the definition of the 'popular' category, discussion then follows on the ways a Brazilian music identity was built after the country's independence in 1822 until the end of the 1920s. An idea of 'popular music' was consolidated throughout the twentieth century, from being associated with rural musical performances of oral tradition to the recorded urban musical genres that were established through radio and television. After exploring the world of mass popular music, the relationships between traditional and modern, the topics of cultural diversity, multiculturalism, and the impact of digitalization, as well as the musical kaleidoscope of the twenty-first century, the Element ends with an insight into music genres in the era of digital platforms.

This Element outlines an overview of popular music made in Brazil, from the nineteenth century to the beginning of the twenty-first century. Initially addressing the definition of the 'popular' category, discussion then follows on the ways a Brazilian music identity was built after the country's independence in 1822 until the end of the 1920s. An idea of 'popular music' was consolidated throughout the twentieth century, from being associated with rural musical performances of oral tradition to the recorded urban musical genres that were established through radio and television. After exploring the world of mass popular music, the relationships between traditional and modern, the topics of cultural diversity, multiculturalism, and the impact of digitalization, as well as the musical kaleidoscope of the twenty-first century, the Element ends with an insight into music genres in the era of digital platforms.

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and KerasKey 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 DescriptionNeural 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 forIf 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.