Arama Sonuçları - ((music introduction) OR (matrices introduction)) features

Real-time Speech and Music Classification by Large Audio Feature Space Extraction Yazar: Eyben, Florian

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From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces Yazar: Grekow, Jacek

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Signal Analysis of Hindustani Classical Music Yazar: Datta, Asoke Kumar, Solanki, Sandeep Singh, Sengupta, Ranjan, Chakraborty, Soubhik, Mahto, Kartik, Patranabis, Anirban

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Musicality of Human Brain through Fractal Analytics Yazar: Ghosh, Dipak, Sengupta, Ranjan, Sanyal, Shankha, Banerjee, Archi

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Computational Chemistry Introduction to the Theory and Applications of Molecular and Quantum Mechanics / Yazar: Lewars, Errol G.

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Engineering Dynamics 2.0 Fundamentals and Numerical Solutions / Yazar: Schmerr, Lester W.

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Downtown Revitalisation and Delta Blues in Clarksdale, Mississippi Lessons for Small Cities and Towns / Yazar: Henshall, John C.

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Fundamentals of Image, Audio, and Video Processing Using MATLAB : With Applications to Pattern Recognition. Yazar: Parekh, Ranjan

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Creative Hubs in Question Place, Space and Work in the Creative Economy /

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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…6.2.5 Regularize and tune your model -- 6.3 Deploy the model -- 6.3.1 Explain your work to stakeholders and set expectations -- 6.3.2 Ship an inference model -- 6.3.3 Monitor your model in the wild -- 6.3.4 Maintain your model -- Summary -- 7 Working with Keras: A deep dive -- 7.1 A spectrum of workflows -- 7.2 Different ways to build Keras models -- 7.2.1 The Sequential model -- 7.2.2 The Functional API -- 7.2.3 Subclassing the Model class -- 7.2.4 Mixing and matching different components -- 7.2.5 Remember: Use the right tool for the job -- 7.3 Using built-in training and evaluation loops -- 7.3.1 Writing your own metrics -- 7.3.2 Using callbacks -- 7.3.3 Writing your own callbacks -- 7.3.4 Monitoring and visualization with TensorBoard -- 7.4 Writing your own training and evaluation loops -- 7.4.1 Training versus inference -- 7.4.2 Low-level usage of metrics -- 7.4.3 A complete training and evaluation loop -- 7.4.4 Make it fast with tf.function -- 7.4.5 Leveraging fit() with a custom training loop -- Summary -- 8 Introduction to deep learning for computer vision -- 8.1 Introduction to convnets -- 8.1.1 The convolution operation -- 8.1.2 The max-pooling operation -- 8.2 Training a convnet from scratch on a small dataset -- 8.2.1 The relevance of deep learning for small-data problems -- 8.2.2 Downloading the data -- 8.2.3 Building the model -- 8.2.4 Data preprocessing -- 8.2.5 Using data augmentation -- 8.3 Leveraging a pretrained model -- 8.3.1 Feature extraction with a pretrained model -- 8.3.2 Fine-tuning a pretrained model -- Summary -- 9 Advanced deep learning for computer vision -- 9.1 Three essential computer vision tasks -- 9.2 An image segmentation example -- 9.3 Modern convnet architecture patterns -- 9.3.1 Modularity, hierarchy, and reuse -- 9.3.2 Residual connections -- 9.3.3 Batch normalization -- 9.3.4 Depthwise separable convolutions.…”
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