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Music Emotion Recognition

by Yi-Hsuan Yang, Homer H. Chen ...

Series series Multimedia Computing, Communication and Intelligence
Providing a comprehensive review of existing work in music emotion developed in psychology and engineering, this book explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with a complete introduction to the essential aspects of MER-including ... Read more

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Providing a comprehensive review of existing work in music emotion developed in psychology and engineering, this book explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with a complete introduction to the essential aspects of MER-including
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Generative Deep Learning

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Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.The book ... Read more

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Python Deep Learning

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by Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants ...

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.Key Features\[\*\] Explore and create intelligent systems using cutting-edge deep learning techniques\[\*\] Implement deep learning algorithms and work with revolutionary libraries in Python\[\*\] Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and ... Read more

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Generative AI with Python and PyTorch

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by Joseph Babcock, Raghav Bali ...

Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and ... Read more

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Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and
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Grokking Machine Learning

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Informed Machine Learning

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Series series Cognitive Technologies
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This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is
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MATLAB for Neuroscientists

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by Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos, Pascal Wallisch ...

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and ... Read more

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MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and
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Introduction To Machine Learning In Quantitative Finance, An

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Series series Advanced Textbooks In Mathematics
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This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional
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Hands-On Neural Networks with Keras

Design and create neural networks using deep learning and artificial intelligence principles

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by Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka ...

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Emotion Recognition

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