Hands-On Music Generation with Magenta PDF Download

Author: Alexandre DuBreuil
Publisher: Packt Publishing Ltd
ISBN: 1838825762
Category : Mathematics
Languages : en
Pages : 348

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Book Description
Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools Key FeaturesLearn how machine learning, deep learning, and reinforcement learning are used in music generationGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with itExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynthBook Description The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this book is the perfect starting point to begin exploring music generation. The book will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser. By the end of this book, you'll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style. What you will learnUse RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequencesUse WaveNet and GAN models to generate instrument notes in the form of raw audioEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequencesPrepare and create your dataset on specific styles and instrumentsTrain your network on your personal datasets and fix problems when training networksApply MIDI to synchronize Magenta with existing music production tools like DAWsWho this book is for This book is for technically inclined artists and musically inclined computer scientists. Readers who want to get hands-on with building generative music applications that use deep learning will also find this book useful. Although prior musical or technical competence is not required, basic knowledge of the Python programming language is assumed.

Author: Niloy Purkait
Publisher: Packt Publishing Ltd
ISBN: 1789533341
Category : Computers
Languages : en
Pages : 450

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Book Description
Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key FeaturesDesign and create neural network architectures on different domains using KerasIntegrate neural network models in your applications using this highly practical guideGet ready for the future of neural networks through transfer learning and predicting multi network modelsBook Description Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization. What you will learnUnderstand the fundamental nature and workflow of predictive data modelingExplore how different types of visual and linguistic signals are processed by neural networksDive into the mathematical and statistical ideas behind how networks learn from dataDesign and implement various neural networks such as CNNs, LSTMs, and GANsUse different architectures to tackle cognitive tasks and embed intelligence in systemsLearn how to generate synthetic data and use augmentation strategies to improve your modelsStay on top of the latest academic and commercial developments in the field of AIWho this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.

Author: Jean-Pierre Briot
Publisher: Springer
ISBN: 3319701630
Category : Computers
Languages : en
Pages : 284

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Book Description
This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Author: Peter Brinkmann
Publisher: "O'Reilly Media, Inc."
ISBN: 1449331394
Category : Computers
Languages : en
Pages : 124

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Book Description
Want to turn your mobile device into a musical instrument? Or equip your game with interactive audio, rather than canned samples? You can do it with Pure Data (Pd), an open source visual programming environment that lets you manipulate digital audio in real time. This concise book shows you how to use Pd—with help from the libpd library—as an easily embeddable and widely portable sound engine. Whether you’re an audio developer looking to create musical apps with sophisticated audio capabilities, or an application developer ready to enhance mobile games with real-time procedural audio, Making Musical Apps introduces you to Pd and libpd, and provides hands-on instructions for creating musical apps for Android and iOS. Get a crash course in Pd, and discover how to generate and control sounds Learn how to create and deploy algorithmic compositions that react to a user’s activity and environment Use Java or Objective-C to integrate Pd and libpd into mobile apps Learn the steps necessary to build libpd-based apps for Android and iOS

Author: Jeffrey Jackovich
Publisher:
ISBN: 9781789806199
Category : Computers
Languages : en
Pages : 254

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Book Description
Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.

Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1788398874
Category : Computers
Languages : en
Pages : 489

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Book Description
Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Book Description Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. What you will learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google’s pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications Who this book is for This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Author: Romain Caudron
Publisher: Packt Publishing Ltd
ISBN: 1787122468
Category : Computers
Languages : en
Pages : 1281

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Book Description
Build your very own stunning characters in Blender from scratch About This Book Packed with illustrations and a lot of tips and tricks to make your scenes come to life Design a complete workflow with Blender to create stunning 3D scenes and films step by step Gain an understanding of how to create and assign materials automatically, working in both the Blender Internal engine as well as in Cycles Who This Book Is For If you are a graphic designer and are looking for a tool to meet your requirements in designing, especially with regards to 3D designing, this course is for you. This course will make use of Blender to meet your design needs. What You Will Learn Understand the basics of 3D and how to navigate your way around the Blender interface Discover the power of the texture paint tool in order to add color to a haunted house Get to know the Cycles render engine by creating different materials for the house and the environment Find the best possible flow for your edge-loops to enhance the character features and to get the best possible range of deformation Mix both the Blender Internal and Cycles rendering engines in order to render materials as quickly as possible Set up light sources and world global illumination Build material interfaces for general use in complex materials by grouping the shaders inside groups Parent and rename the nodes to better organize the Node Editor window In Detail Blender is a powerful, stable tool with an integral workflow that will allow you to understand 3D creation with ease. With its integrated game engine and use of the Python language, it is an efficient choice for many productions, including 3D animated or live action films, architecture, research, and even game creation. Blender has an active community that contributes to expanding its functionalities. Today, it is used in many professional products and by many companies. Throughout Blender for Designers, you will create many types of complete projects using a step-by-step approach. Start by getting to know the modeling tools available in Blender to create a 3D robot toy, and discover more advanced techniques such as sculpting and retopology by creating an alien character. Move on in the second module to engage with the workflow used to create characters. Run through the process from modeling to the rendering stages, using the tools of the latest official release of Blender. The last module will teach you how to utilize the power of the Blender series to create a wide variety of materials, textures, and effects using the Cycles rendering engine. You will learn about node-based shader creation, and master Cycles through step-by-step, recipe-based advice. Start small by rendering the textures of stones and water, then scale things up to massive landscapes of mountains and oceans. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Blender 3D By Example By Romain Caudron and Pierre-Armand Nicq Blender 3D Cookbook By Enrico Valenza Blender Cycles: Materials and Textures Cookbook - Third Edition By Enrico Valenza Style and approach The course starts with a step-by-step approach to creating concert projects and help you understand the basics of it. With the guided explanation throughout this, each topic is explained with an example.

Author: Francois Chollet
Publisher: Simon and Schuster
ISBN: 1638352046
Category : Computers
Languages : en
Pages : 597

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Book Description
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Author: Ray Rischpater
Publisher: Packt Pub Limited
ISBN: 9781784398675
Category : Computers
Languages : en
Pages : 264

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Book Description
This book is great for developers who are new to Qt and Qt Creator and who are interested in harnessing the power of Qt for cross-platform development. If you have basic experience programming in C++, you have what it takes to create engaging cross-platform applications using Qt and Qt Creator!

Author: George McKay
Publisher: University of Michigan Press
ISBN: 0472120042
Category : Music
Languages : en
Pages : 230

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Book Description
Given the explosion in recent years of scholarship exploring the ways in which disability is manifested and performed in numerous cultural spaces, it’s surprising that until now there has never been a single monograph study covering the important intersection of popular music and disability. George McKay’s Shakin’ All Over is a cross-disciplinary examination of the ways in which popular music performers have addressed disability: in their songs, in their live performances, and in various media presentations. By looking closely into the work of artists such as Johnny Rotten, Neil Young, Johnnie Ray, Ian Dury, Teddy Pendergrass, Curtis Mayfield, and Joni Mitchell, McKay investigates such questions as how popular music works to obscure and accommodate the presence of people with disabilities in its cultural practice. He also examines how popular musicians have articulated the experiences of disability (or sought to pass), or have used their cultural arena for disability advocacy purposes.