Tabla de Contenidos: “…Selecting algorithms based on function -- Using Spark to generate real-time big data analytics -- Chapter 4 Math, Probability, and Statistical Modeling -- Exploring Probability and Inferential Statistics -- Probability distributions -- Conditional probability with Naïve Bayes -- Quantifying Correlation -- Calculating correlation with Pearson's r -- Ranking variable-pairs using Spearman's rank correlation -- Reducing Data Dimensionality with Linear Algebra -- Decomposing data to reduce dimensionality -- Reducing dimensionality with factor analysis -- Decreasing dimensionality and removing outliers with PCA -- Modeling Decisions with Multiple Criteria Decision-Making -- Turning to traditional MCDM -- Focusing on fuzzy MCDM -- Introducing Regression Methods -- Linear regression -- Logistic regression -- Ordinary least squares (OLS) regression methods -- Detecting Outliers -- Analyzing extreme values -- Detecting outliers with univariate analysis -- Detecting outliers with multivariate analysis -- Introducing Time Series Analysis -- Identifying patterns in time series -- Modeling univariate time series data -- Chapter 5 Grouping Your Way into Accurate Predictions -- Starting with Clustering Basics -- Getting to know clustering algorithms -- Examining clustering similarity metrics -- Identifying Clusters in Your Data -- Clustering with the k-means algorithm -- Estimating clusters with kernel density estimation (KDE) -- Clustering with hierarchical algorithms -- Dabbling in the DBScan neighborhood -- Categorizing Data with Decision Tree and Random Forest Algorithms -- Drawing a Line between Clustering and Classification -- Introducing instance-based learning classifiers -- Getting to know classification algorithms -- Making Sense of Data with Nearest Neighbor Analysis -- Classifying Data with Average Nearest Neighbor Algorithms…”

Tabla de Contenidos: “…-- TensorFlow core -- Code warm-up - Hello TensorFlow -- Tensors -- Constants -- Operations -- Placeholders -- Creating tensors from Python objects -- Variables -- Tensors generated from library functions -- Populating tensor elements with the same values -- Populating tensor elements with sequences -- Populating tensor elements with a random distribution -- Getting Variables with tf.get_variable() -- Data flow graph or computation graph -- Order of execution and lazy loading -- Executing graphs across compute devices - CPU and GPGPU -- Placing graph nodes on specific compute devices -- Simple placement -- Dynamic placement -- Soft placement -- GPU memory handling -- Multiple graphs -- TensorBoard -- A TensorBoard minimal example -- TensorBoard details -- Summary -- Chapter 2: High-Level Libraries for TensorFlow -- TF Estimator - previously TF Learn -- TF Slim -- TFLearn -- Creating the TFLearn Layers -- TFLearn core layers -- TFLearn convolutional layers -- TFLearn recurrent layers -- TFLearn normalization layers -- TFLearn embedding layers -- TFLearn merge layers -- TFLearn estimator layers -- Creating the TFLearn Model -- Types of TFLearn models -- Training the TFLearn Model -- Using the TFLearn Model -- PrettyTensor -- Sonnet -- Summary -- Chapter 3: Keras 101 -- Installing Keras -- Neural Network Models in Keras -- Workflow for building models in Keras -- Creating the Keras model -- Sequential API for creating the Keras model -- Functional API for creating the Keras model -- Keras Layers -- Keras core layers -- Keras convolutional layers -- Keras pooling layers -- Keras locally-connected layers -- Keras recurrent layers -- Keras embedding layers -- Keras merge layers…”

Tabla de Contenidos: “…Differences and similarities between OpenGL and Vulkan, reprised -- Summary -- Practical sessions -- Additional resources -- Chapter 4: Working with Shaders -- Technical requirements -- Shader basics -- GLM, the OpenGL Mathematics library -- GLM data types and basic operations -- GLM transformations -- Vertex data transfer to the GPU -- Switching shaders at runtime -- Creating a new set of shaders -- Binding the shader switching to a key -- The shader switch in the draw call -- Shader switching in Vulkan -- Sending additional data to the GPU -- Using uniform buffers to upload constant data -- Creating a uniform buffer -- Shader changes to use the data in the buffer -- Preparing and uploading data -- Using uniform buffers in Vulkan -- Using push constants in Vulkan -- Summary -- Practical sessions -- Additional resources -- Chapter 5: Adding Dear ImGui to Show Valuable Information -- Technical requirements -- What is Dear ImGui? …”

Tabla de Contenidos: “…-- Importing data -- Importing data into pandas from Python data structures -- Importing data into pandas from a flat file -- Importing data into pandas from a database -- Common operations on DataFrames -- Adding columns -- Adding blank or user-initialized columns -- Adding new columns by transforming existing columns -- Dropping columns -- Applying functions to multiple columns -- Combining DataFrames -- Converting DataFrame columns to lists -- Getting and setting DataFrame values -- Getting/setting values using label-based indexing with loc -- Getting/setting values using integer-based labeling with iloc -- Getting/setting multiple contiguous values using slicing -- Fast getting/setting of scalar values using at and iat -- Other operations -- Filtering rows using Boolean indexing -- Sorting rows -- SQL-like operations -- Getting aggregate row COUNTs -- Joining DataFrames -- Introduction to scikit-learn -- Sample data -- Data preprocessing -- One-hot encoding of categorical variables -- Scaling and centering -- Binarization -- Imputation -- Feature-selection -- Machine learning algorithms -- Generalized linear models -- Ensemble methods -- Additional machine learning algorithms…”

Tabla de Contenidos: “…The Hitchhiker's Guide to Concurrency -- Don't Panic -- Concurrency Concepts -- Scalability -- Fault Tolerance -- Concurrency Implementation -- Not Entirely Unlike Linear Scaling -- So Long and Thanks for All the Fish! …”

Tabla de Contenidos: “…-- 10.3 Function Definition -- 10.4 The return Statement -- 10.5 Types of Functions -- 10.6 Call by Value and Reference -- 10.7 Function Returning More Values -- 10.8 Function as an Argument -- 10.9 Function with Operators -- 10.10 Function and Decision Statements -- 10.11 Function and Loop Statements -- 10.12 Functions with Arrays and Pointers -- 10.13 Recursion -- 10.14 Types of Recursion -- 10.15 Rules for Recursive Function -- 10.16 Direct Recursion -- 10.17 Indirect Recursion -- 10.18 Recursion Versus Iterations -- 10.19 The Towers of Hanoi -- 10.20 Advantages and Disadvantages of Recursion -- 10.21 Efficiency of Recursion -- 10.22 Library Function -- Summary -- Exercises…”

Tabla de Contenidos: “…Rig Finalizing -- Selection Sets -- Rotation Order -- How Modes Affect Animation -- Channel Lock and Limit -- Visibility Layers -- Colors -- Scene Cleanup -- Pose Library -- 33. Animation -- Linking and Library Overrides -- Relative Versus Absolute Paths -- Forcing a Linked Object to Update -- Framerate -- Keyframes and Keying Sets -- Actions -- In-Between Tools -- NLA -- Discontinuity (Euler) Filter -- Preview Rendering -- Rotation Order and Mode -- 34. …”

Tabla de Contenidos:

Tabla de Contenidos: “…21.4 Amortized Performance and Average Performance -- 21.5 Two Principles: Reducing Overhead and Managing Memory -- 21.6 Performance Tip: Use Numerical Libraries When Applicable -- 21.7 Performance Tip: Delete Large Structures You Don't Need -- 21.8 Performance Tip: Use Built-In Functions When Possible -- 21.9 Performance Tip: Avoid Superfluous Function Calls -- 21.10 Performance Tip: Avoid Creating Large New Objects -- 21.11 Further Reading -- 21.12 Glossary -- Part III Specialized or Advanced Topics -- Chapter 22 Computer Memory and Data Structures -- 22.1 Virtual Memory, the Stack, and the Heap -- 22.2 Example C Program -- 22.3 Data Types and Arrays in Memory -- 22.4 Structs -- 22.5 Pointers, the Stack, and the Heap -- 22.6 Key Data Structures -- 22.7 Further Reading -- 22.8 Glossary -- Chapter 23 Maximum Likelihood Estimation and Optimization -- 23.1 Maximum Likelihood Estimation -- 23.2 A Simple Example: Fitting a Line -- 23.3 Another Example: Logistic Regression -- 23.4 Optimization -- 23.5 Gradient Descent and Convex Optimization -- 23.6 Convex Optimization -- 23.7 Stochastic Gradient Descent -- 23.8 Further Reading -- 23.9 Glossary -- Chapter 24 Advanced Classifiers -- 24.1 A Note on Libraries -- 24.2 Basic Deep Learning -- 24.3 Convolutional Neural Networks -- 24.4 Different Types of Layers. …”

Tabla de Contenidos: “…-- Working with Promises -- Parallel collections -- Summary -- Chapter 14: Programming with Reactive Extensions -- Reactive programming -- Reactive extensions -- React to RxScala -- Creating Observables -- Summary -- Chapter 15: Testing in Scala -- The why and what of TDD -- The process of TDD -- Step 1 - write a test that will fail -- Step 2 - write code to pass the failing test -- Step 3 - refactor the code to improve the quality -- Step 4 - repeat steps 1 to 3 -- Scenario -- Behavior driven development (BDD) -- ScalaTest -- Setting up for testing -- Testing in style using ScalaTest -- Assertions -- Matchers -- ScalaMock - a native library to mock objects -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…-- Advantages over traditional shallow methods -- Impact of deep learning -- The motivation of deep architecture -- The neural viewpoint -- The representation viewpoint -- Distributed feature representation -- Hierarchical feature representation -- Applications -- Lucrative applications -- Success stories -- Deep learning for business -- Future potential and challenges -- Summary -- Chapter 2: Getting Yourself Ready for Deep Learning -- Basics of linear algebra -- Data representation -- Data operations -- Matrix properties -- Deep learning with GPU -- Deep learning hardware guide -- CPU cores -- RAM size -- Hard drive -- Cooling systems -- Deep learning software frameworks -- TensorFlow - a deep learning library -- Caffe -- MXNet -- Torch -- Theano -- Microsoft Cognitive Toolkit -- Keras -- Framework comparison -- Setting up deep learning on AWS -- Setup from scratch -- Setup using Docker -- Summary -- Chapter 3: Getting Started with Neural Networks -- Multilayer perceptrons -- The input layer -- The output layer -- Hidden layers -- Activation functions -- Sigmoid or logistic function -- Tanh or hyperbolic tangent function -- ReLU -- Leaky ReLU and maxout -- Softmax -- Choosing the right activation function -- How a network learns -- Weight initialization -- Forward propagation -- Backpropagation -- Calculating errors -- Backpropagation -- Updating the network -- Automatic differentiation -- Vanishing and exploding gradients -- Optimization algorithms -- Regularization -- Deep learning models -- Convolutional Neural Networks -- Convolution -- Pooling/subsampling -- Fully connected layer -- Overall…”

Tabla de Contenidos: “…Analyzing an Audio File -- Zooming In on Problems -- Fixing the Problems Soundtrack Pro Detects -- Working with Actions -- Locating an Action -- Reordering Actions -- Removing Actions -- Reducing Noise -- Exploring Noise -- Setting a Noise Print -- Using the Reduce Noise Dialog -- Bypassing Noise Reduction to Hear Your Original Sound -- Adjusting the Noise Reduction Level -- Applying Noise Reduction -- Refining Noise Reduction Settings -- Rippling Your Changes to Final Cut Pro -- Using Soundtrack Pro Scripts to Speed Up Your Work -- Lesson Review -- Lesson 5 Editing in Frequency Spectrum View -- Using Frequency Spectrum View -- Exploring the Frequency Spectrum View -- Linear Versus Logarithmic Scales -- Controlling the Spectrum -- Setting the Frequency Range -- Adjusting the Spectrum Power Controls -- Adjusting View Color Scheme -- Resetting the Frequency Spectrum View -- Working with Frequency -- Removing Frequencies -- Finishing Up -- Lesson Review -- Lesson 6 Recording in Soundtrack Pro -- Opening a Multitrack Project -- Using the Timecode HUD -- Working with Tracks -- Creating Tracks -- Reordering Tracks -- Deleting Tracks -- Naming Tracks -- Locking Tracks -- Setting Up for Recording -- Choosing a Track to Record -- Choosing a Recording Input -- Choosing a Channel Configuration -- Adjusting the Channel Setting -- Displaying Recording Settings in the Track Header -- Recording a Single Take -- Choosing a Recording Destination -- Arming a Track for Recording -- Setting a Recording Level -- Resetting the Peak Indicator -- Recording a Take -- Collecting the Project -- Using Punch Recording -- Lesson Review -- Lesson 7 Editing Dialogue -- Using Soundtrack Pro's Editing Tools -- Using the Selection Tool -- Using the Blade Tool -- Deleting Clips -- Using the Timeslice Tool -- Arranging Clips -- Disabling Snapping Mode -- Moving Clips in a Single Track…”

Tabla de Contenidos: “…Special Effects -- Creating complex effects with particle systems -- Adding a dust particle effect to the player character -- Moving the clouds -- Smoothing with Linear interpolation (Lerp) -- Letting the rocks float on the water -- Adding parallax scrolling to the mountains in the background -- Enhancing the game screen's GUI -- Event - player lost a life -- Event - score increased -- Summary -- 9. …”

Tabla de Contenidos: “…7.3.3 Localization, Mapping, and SLAM -- 7.3.4 Control Strategies -- 7.3.5 Planning Routes and Scheduling -- 7.4 Wheeled Mobile Robots in Industry -- 7.4.1 About -- 7.4.2 Setup -- 7.4.3 Sensors -- 7.4.4 Localization and Mapping -- 7.4.5 Control -- 7.4.6 Path Planning -- 7.4.7 Decision Making -- 7.5 Wheeled Mobile Robots in Domestic Environments -- 7.5.1 About -- 7.5.2 Setup -- 7.5.3 Sensors -- 7.5.4 Localization and Mapping -- 7.5.5 Path Planning -- 7.5.6 Control -- 7.5.7 Decision Making -- 7.6 Assistive Mobile Robots in Walking Rehabilitation Therapy -- 7.6.1 About -- 7.6.2 Setup -- 7.6.3 Sensors -- 7.6.4 Localization and Mapping -- 7.6.5 Control -- 7.6.6 Path Planning -- References -- Chapter 8: Project Examples for Laboratory Practice -- 8.1 Introduction -- 8.2 Localization Based on Bayesian Filter Within a Network of Paths and Crossroads -- 8.2.1 Introduction -- 8.2.2 Line Following -- 8.2.3 Odometry -- 8.2.4 Map Building -- 8.2.5 Localization -- 8.3 Localization Based on Extended Kalman Filter Using Indoor Global Positioning System and Odometry -- 8.3.1 Introduction -- 8.3.2 Experimental Setup -- 8.3.3 Extended Kalman Filter -- 8.4 Particle-Filter-Based Localization in a Pattern of Colored Tiles -- 8.4.1 Introduction -- 8.4.2 Experimental Setup -- 8.4.3 Manual Control -- 8.4.4 Wheel Odometry -- 8.4.5 Color Sensor Calibration -- 8.4.6 Particle Filter -- 8.5 Control of Vehicles in a Linear Formation -- 8.5.1 Introduction -- 8.5.2 Localization Using Odometry -- 8.5.3 Estimating Reference Trajectory -- 8.5.4 Linear Formation Control -- 8.6 Motion and Strategy Control of Multiagent Soccer Robots -- 8.6.1 Introduction -- 8.6.2 Motion Control -- 8.6.3 Behavior-Based Agent Operation -- 8.6.4 Multiagent Game Strategy -- 8.7 Image-Based Control of a Mobile Robot -- 8.7.1 Introduction -- 8.7.2 Experimental Setup -- 8.7.3 Position-Based Visual Servoing…”

Tabla de Contenidos: “…Backup and recovery for OnDemand z/OS -- 12.1 Backup and recovery overview -- 12.2 Library Server backup and recovery -- 12.2.1 OnDemand software -- 12.2.2 OnDemand server information…”

“…Picasso era español y por eso, según dicen, comenzó a dibujar antes que a hablar. Cuando niño, se sintió atraído de manera instintiva hacia las herramientas del artista. …”
Texto completo en Odilo

“…El principal contenido de este libro tiene como referencia los criterios del Marco Común Europeo en las áreas relacionadas con las capacidades comunicativas de comprender y de hablar en el nivel A2 intermedio. La obra contiene diez temas con diferentes actividades y textos teatrales. …”
Texto completo en Odilo

Tabla de Contenidos: “…PUNTOS IMPORTANTES EN UNA INTERPRETACIÓN, Cristina Cela Gutiérrez Introducción 1. Hablar 2. Intraducibilidad 3. Argumentación 4. Citas y alegaciones 5. …”
Texto completo en Odilo

“…Si durante siglos y decenas de años su soporte exclusivo era el papel, hoy adopta también otros formatos digitales, incluso se puede hablar con pertinencia de prensa y diarios hablados, de semanarios audiovisuales, porque todos estos modelos técnicos forman parte del proceso instrumental de comunicación de masas que sin duda evoluciona. …”
Enlace del recurso

Tabla de Contenidos: