Tabla de Contenidos: “…Conviene sin embargo tener en cuenta que en elDiccionario de la Lengua Española, de la Real Academia, drae, publicado en 2001, hay 2261 artículos en los que se menciona, normalmente como etimología, el francés o el provenzal1, y sólo 819 en los que de alguna forma se explica, por el inglés2, el origen de una voz española. …”
Enlace del recurso

Tabla de Contenidos: “…Lo que pasaron los dos españoles en su viaje hasta que llegaron al real -- Capítulo XV. Salen treinta lanzas con el socorro del bizcocho en pos del gobernador -- Capítulo XVI. …”

Tabla de Contenidos: “…4.5 Typical Applications of Tensors -- 4.5.1 Scalar -- 4.5.2 Vector -- 4.5.3 Matrix -- 4.5.4 Three-Dimensional Tensor -- 4.5.5 Four-Dimensional Tensor -- 4.6 Indexing and Slicing -- 4.6.1 Indexing -- 4.6.2 Slicing -- 4.6.3 Slicing Summary -- 4.7 Dimensional Transformation -- 4.7.1 Reshape -- 4.7.2 Add and Delete Dimensions -- 4.7.3 Swap Dimensions -- 4.7.4 Copy Data -- 4.8 Broadcasting -- 4.9 Mathematical Operations -- 4.9.1 Addition, Subtraction, Multiplication and Division -- 4.9.2 Power Operations -- 4.9.3 Exponential and Logarithmic Operations -- 4.9.4 Matrix Multiplication -- 4.10 Hands-On Forward Propagation -- Chapter 5: Advanced TensorFlow -- 5.1 Merge and Split -- 5.1.1 Merge -- 5.1.2 Split -- 5.2 Common Statistics -- 5.2.1 Norm -- 5.2.2 Max, Min, Mean, and Sum -- 5.3 Tensor Comparison -- 5.4 Fill and Copy -- 5.4.1 Fill -- 5.4.2 Copy -- 5.5 Data Limiting -- 5.6 Advanced Operations -- 5.6.1 tf.gather -- 5.6.2 tf.gather_nd -- 5.6.3 tf.boolean_mask -- 5.6.4 tf.where -- 5.6.5 tf.scatter_nd -- 5.6.6 tf.meshgrid -- 5.7 Load Classic Datasets -- 5.7.1 Shuffling -- 5.7.2 Batch Training -- 5.7.3 Preprocessing -- 5.7.4 Epoch Training -- 5.8 Hands-On MNIST Dataset -- Chapter 6: Neural Networks -- 6.1 Perceptron -- 6.2 Fully Connected Layer -- 6.2.1 Tensor Mode Implementation -- 6.2.2 Layer Implementation -- 6.3 Neural Network -- 6.3.1 Tensor Mode Implementation -- 6.3.2 Layer Mode Implementation -- 6.3.3 Optimization -- 6.4 Activation function -- 6.4.1 Sigmoid -- 6.4.2 ReLU -- 6.4.3 LeakyReLU -- 6.4.4 Tanh -- 6.5 Design of Output Layer -- 6.5.1 Common Real Number Space -- 6.5.2 [0, 1] Interval -- 6.5.3 [0,1] Interval with Sum 1 -- 6.5.4 (-1, 1) Interval -- 6.6 Error Calculation -- 6.6.1 Mean Square Error Function -- 6.6.2 Cross-Entropy Error Function -- 6.7 Types of Neural Networks -- 6.7.1 Convolutional Neural Network -- 6.7.2 Recurrent Neural Network…”

Tabla de Contenidos: “…5.3.2.1 Innovation Processes -- 5.3.2.2 Moving Average Processes -- 5.3.2.3 Autoregressive Processes -- 5.3.2.4 ARMA Processes -- 5.3.3 Conditional Heteroskedasticity Models -- 5.3.3.1 ARCH Processes -- 5.3.3.2 GARCH Processes -- 5.3.3.3 ARCH(∞) Model -- 5.3.3.4 Asymmetric GARCH Processes -- 5.3.3.5 The Moment Generating function -- 5.3.3.6 Parameter Estimation -- 5.3.3.7 Fitting the GARCH(1,1) Model -- 5.3.4 Continuous Time Processes -- 5.3.4.1 The Brownian Motion -- 5.3.4.2 Diffusion Processes and Itô's Lemma -- 5.3.4.3 The Geometric Brownian Motion -- 5.3.4.4 Girsanov's Theorem -- 5.4 Multivariate Time Series Models -- 5.4.1 MGARCH Models -- 5.4.2 Covariance in MGARCH Models -- 5.5 Time Series Stylized Facts -- Chapter 6 Prediction -- 6.1 Methods of Prediction -- 6.1.1 Moving Average Predictors -- 6.1.1.1 One‐Sided Moving Average -- 6.1.1.2 Exponential Moving Average -- 6.1.2 State Space Predictors -- 6.1.2.1 Linear Regression -- 6.1.2.2 Kernel Regression -- 6.2 Forecast Evaluation -- 6.2.1 The Sum of Squared Prediction Errors -- 6.2.1.1 Out‐of‐Sample Sum of Squares -- 6.2.1.2 In‐Sample Sum of Squares -- 6.2.1.3 Visual Diagnostics -- 6.2.2 Testing the Prediction Accuracy -- 6.2.2.1 Diebold-Mariano Test -- 6.2.2.2 Tests Using Sample Correlation and Covariance -- 6.3 Predictive Variables -- 6.3.1 Risk Indicators -- 6.3.1.1 Default Spread -- 6.3.1.2 Credit Spreads -- 6.3.1.3 Volatility Indexes -- 6.3.2 Interest Rate Variables -- 6.3.2.1 Term Spread -- 6.3.2.2 Real Yield -- 6.3.3 Stock Market Indicators -- 6.3.3.1 Dividend Price Ratio and Dividend Yield -- 6.3.3.2 Valuation in Stock Markets -- 6.3.3.3 Relative Valuation -- 6.3.4 Sentiment Indicators -- 6.3.4.1 Purchasing Managers Index -- 6.3.4.2 Investor and Consumer Sentiment -- 6.3.5 Technical Indicators -- 6.4 Asset Return Prediction -- 6.4.1 Prediction of S&amp -- P 500 Returns -- 6.4.1.1 S&amp…”

Tabla de Contenidos: “…Data types -- 5.1 Object names -- 5.2 Data type mapping -- 5.3 NULL values -- 5.4 Disk considerations -- 5.5 Character types -- 5.5.1 Truncation -- 5.5.2 NCHAR data type -- 5.5.3 VARCHAR data type -- 5.5.4 TEXT data type -- 5.6 Numerical data types -- 5.6.1 Numerical limits -- 5.7 DECIMAL -- 5.7.1 MONEY data type -- 5.7.2 SERIAL and SERIAL8 -- 5.8 Date and time types -- 5.8.1 DATE data type -- 5.8.2 DATETIME, TIME, and TIMESTAMP data types -- 5.8.3 INTERVAL data type -- 5.9 FLOAT -- 5.10 REAL or SMALLFLOAT -- 5.11 LOB data types -- 5.12 Sequence objects -- 5.13 Other object limits in DB2 -- 5.14 DB2 manuals -- Chapter 6. …”

Tabla de Contenidos: “…The development process -- 4.1 Development process basics -- 4.1.1 Definition of a development process -- 4.1.2 Importance of a development process -- 4.1.3 Realization of a development process -- 4.1.4 Development process principles -- 4.2 Starting a project -- 4.2.1 Understanding your business today -- 4.2.2 Where do you want to go -- 4.2.3 An initial roadmap of how to get there -- 4.3 Understanding and planning a project -- 4.4 Building a solution -- 4.5 Project hand-over -- 4.6 RealEstate application architecture -- 4.6.1 Component-based architecture -- 4.6.2 Layered design -- 4.6.3 Package structure -- 4.6.4 Naming conventions -- Part 2 The sample solution -- Chapter 5. …”

Tabla de Contenidos: “…3.3 Resolutions in SAR -- 3.4 SAR Image Formation -- 3.5 Range Compression -- 3.5.1 Matched Filter -- 3.5.1.1 Computing Matched Filter Output via Fourier Processing -- 3.5.1.2 Example for Matched Filtering -- 3.5.2 Ambiguity Function -- 3.5.2.1 Relation to Matched Filter -- 3.5.2.2 Ideal Ambiguity Function -- 3.5.2.3 Rectangular-Pulse Ambiguity Function -- 3.5.2.4 LFM-Pulse Ambiguity Function -- 3.5.3 Pulse Compression -- 3.5.3.1 Detailed Processing of Pulse Compression -- 3.5.3.2 Bandwidth, Resolution, and Compression Issues for LFM Signal -- 3.5.3.3 Pulse Compression Example -- 3.6 Azimuth Compression -- 3.6.1 Processing in Azimuth -- 3.6.2 Azimuth Resolution -- 3.6.3 Relation to ISAR -- 3.7 SAR Imaging -- 3.8 SAR Focusing Algorithms -- 3.8.1 RDA -- 3.8.1.1 Range Compression in RDA -- 3.8.1.2 Azimuth Fourier Transform -- 3.8.1.3 Range Cell Migration Correction -- 3.8.1.4 Azimuth Compression -- 3.8.1.5 Simulated SAR Imaging Example -- 3.8.1.6 Drawbacks of RDA -- 3.8.2 Chirp Scaling Algorithm -- 3.8.3 The ω-kA -- 3.8.4 Back-Projection Algorithm -- 3.9 Example of a Real SAR Imagery -- 3.10 Problems in SAR Imaging -- 3.10.1 Range Migration and Range Walk -- 3.10.2 Motion Errors -- 3.10.3 Speckle Noise -- 3.11 Advanced Topics in SAR -- 3.11.1 SAR Interferometry -- 3.11.2 SAR Polarimetry -- 3.12 Matlab Codes -- References -- Chapter 4 Inverse Synthetic Aperture Radar Imaging and Its Basic Concepts -- 4.1 SAR versus ISAR -- 4.2 The Relation of Scattered Field to the Image Function in ISAR -- 4.3 One-Dimensional (1D) Range Profile -- 4.4 1D Cross-Range Profile -- 4.5 Two-Dimensional (2D) ISAR Image Formation (Small Bandwidth, Small Angle) -- 4.5.1 Resolutions in ISAR -- 4.5.1.1 Range Resolution -- 4.5.1.2 Cross-Range Resolution: -- 4.5.2 Range and Cross-Range Extends -- 4.5.3 Imaging Multibounces in ISAR -- 4.5.4 Sample Design Procedure for ISAR…”

Tabla de Contenidos: “…. -- The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung -- Lesions on CT Scans: Ready for the "Real World"? -- Reprinted from: Diagnostics 2023, 15, 357, doi:10.3390/cancers15020357 385 -- Jang Yoo, Jaeho Lee, Miju Cheon, Sang-Keun Woo, Myung-Ju Ahn, Hong Ryull Pyo, -- Yong Soo Choi, et al. -- Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to -- Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell -- Lung Cancer -- Reprinted from: Cancers 2022, 14, 1987, doi:10.3390/cancers14081987 399 -- Gi Hwan Kim, Yong Mee Cho, So-Woon Kim, Ja-Min Park, Sun Young Yoon, Gowun Jeong, -- Dong-Myung Shin, et al.…”