Tabla de Contenidos: “…Según Valdenebro, no 217, el autor es seud. de Juan de Ribas Carrasquillo y el pie de imprenta es falso, pues se imprimió en Córdoba por Andrés Carrillo. …”

Tabla de Contenidos: “…5.2 Learning a Task-Speci c Deep Architecture for Clustering -- 5.2.1 Introduction -- 5.2.2 Related Work -- 5.2.2.1 Sparse Coding for Clustering -- 5.2.2.2 Deep Learning for Clustering -- 5.2.3 Model Formulation -- 5.2.3.1 TAGnet: Task-speci c And Graph-regularized Network -- 5.2.3.2 Clustering-Oriented Loss Functions -- 5.2.3.3 Connections to Existing Models -- 5.2.4 A Deeper Look: Hierarchical Clustering by DTAGnet -- 5.2.5 Experiment Results -- 5.2.5.1 Datasets and Measurements -- 5.2.5.2 Experiment Settings -- 5.2.5.3 Comparison Experiments and Analysis -- Bene ts of the Task-speci c Deep Architecture -- Effects of Graph Regularization -- Scalability and Robustness -- 5.2.5.4 Hierarchical Clustering on CMU MultiPIE -- 5.2.6 Conclusion -- References -- 6 Signal Processing -- 6.1 Deeply Optimized Compressive Sensing -- 6.1.1 Background -- 6.1.2 An End-to-End Optimization Model of CS -- 6.1.3 DOCS: Feed-Forward and Jointly Optimized CS -- Complexity -- Related Work -- 6.1.4 Experiments -- Settings -- Simulation -- Reconstruction Error -- Ef ciency -- Experiments on Image Reconstruction -- 6.1.5 Conclusion -- 6.2 Deep Learning for Speech Denoising -- 6.2.1 Introduction -- 6.2.2 Neural Networks for Spectral Denoising -- 6.2.2.1 Network Architecture -- 6.2.2.2 Implementation Details -- Activation Function -- Cost Function -- Training Strategy -- 6.2.2.3 Extracting Denoised Signals -- 6.2.2.4 Dealing with Gain -- 6.2.3 Experimental Results -- 6.2.3.1 Experimental Setup -- 6.2.3.2 Network Structure Analysis -- 6.2.3.3 Analysis of Robustness to Variations -- 6.2.3.4 Comparison with NMF -- 6.2.4 Conclusion and Future Work -- References -- 7 Dimensionality Reduction -- 7.1 Marginalized Denoising Dictionary Learning with Locality Constraint -- 7.1.1 Introduction -- 7.1.2 Related Works -- 7.1.2.1 Dictionary Learning -- 7.1.2.2 Auto-encoder…”

Acceso a índice e introducción

Tabla de Contenidos: “…General Lemason, digan los conversos sus culpas particulares de inobservancias en el Capitulo, estando en pie como los Monges... (h. 293 r.-301 v.). 25. Articulo 20... que puedan los PP. …”

“…Qui ne s’est pas arrêté au pied de cette montagne pour admirer la couleur fauve des vignes, le doré des hêtres, le rouge des cerisiers, le feu des érables en automne ? …”

“…Un niño tiene manos pequeñas, pies pequeños y orejas pequeñas, pero no por eso tiene ideas pequeñas. …”

“…En sus sueæos, Jean-Marc es una estrella del escenario y de la pantalla, y un novelista de Øxito que tiene a las mujeres rendidas a sus pies. En realidad, es un don nadie, un marido insignificante y un padre fracasado…”
Trailer

“…Urko, el osezno, vive con su hermano Martín en una cueva a los pies de la montaña. Es un oso pequeño y nervioso. …”

“…Para evitar que los indios Pies Negros le corten la cabellera, Mitchell se casa con Kamiah, la hija del jefe de la tribu…”

Tabla de Contenidos: “…Jesús resucita a un niño -- VIII. Jesús cura el pie de un niño -- IX. Jesús lleva el agua en su ropa -- X. …”

Tabla de Contenidos: “…TENDRÍAS AL UNIVERSO ENTERO A PIE DE CAMA -- XXVI. SED NON SATIATA - XXVI. SED NON SATIATA -- XXVII. …”

Tabla de Contenidos:

Tabla de Contenidos: “…Tendance d'évolution des déplacements à pied et à bicyclette -- 1.1.5. Tendance à la congestion -- 1.1.6. …”

Tabla de Contenidos: “…8.7.7 Filtering using queries -- 8.7.8 Discarding data with projection -- 8.7.9 Sorting large data sets -- 8.8 Achieving better data throughput -- 8.8.1 Optimize your code -- 8.8.2 Optimize your algorithm -- 8.8.3 Processing data in parallel -- Summary -- Chapter 9: Practical data analysis -- 9.1 Expanding your toolkit -- 9.2 Analyzing the weather data -- 9.3 Getting the code and data -- 9.4 Basic data summarization -- 9.4.1 Sum -- 9.4.2 Average -- 9.4.3 Standard deviation -- 9.5 Group and summarize -- 9.6 The frequency distribution of temperatures -- 9.7 Time series -- 9.7.1 Yearly average temperature -- 9.7.2 Rolling average -- 9.7.3 Rolling standard deviation -- 9.7.4 Linear regression -- 9.7.5 Comparing time series -- 9.7.6 Stacking time series operations -- 9.8 Understanding relationships -- 9.8.1 Detecting correlation with a scatter plot -- 9.8.2 Types of correlation -- 9.8.3 Determining the strength of the correlation -- 9.8.4 Computing the correlation coefficient -- Summary -- Chapter 10: Browser-based visualization -- 10.1 Expanding your toolkit -- 10.2 Getting the code and data -- 10.3 Choosing a chart type -- 10.4 Line chart for New York City temperature -- 10.4.1 The most basic C3 line chart -- 10.4.2 Adding real data -- 10.4.3 Parsing the static CSV file -- 10.4.4 Adding years as the X axis -- 10.4.5 Creating a custom Node.js web server -- 10.4.6 Adding another series to the chart -- 10.4.7 Adding a second Y axis to the chart -- 10.4.8 Rendering a time series chart -- 10.5 Other chart types with C3 -- 10.5.1 Bar chart -- 10.5.2 Horizontal bar chart -- 10.5.3 Pie chart -- 10.5.4 Stacked bar chart -- 10.5.5 Scatter plot chart -- 10.6 Improving the look of our charts -- 10.7 Moving forward with your own projects -- Summary -- Chapter 11: Server-side visualization -- 11.1 Expanding your toolkit -- 11.2 Getting the code and data…”

Tabla de Contenidos: “…Bonaventure que est quasi quedam rregula.. . omnium pie et spiritualiter in Christo vivere volencium.. . …”