Tabla de Contenidos: “….); Un ""modelo emergente"" para generar (...); Bibliografía; LOS AUTORES…”

Tabla de Contenidos: “…El progresismo de John Dewey / Virginia Guichot-Reina -- María Montessori y otras aportaciones italianas / José Manuel Prellezo García -- El método Decroly / Pedro Luis Moreno Martínez -- La modernización de la escuela infantil en Cataluña / Bernat Sureda García -- La escuela de Ginebra: Claparède, Piaget, Audemars y Lafendel / Angel C. …”
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Tabla de Contenidos: “…Identity development, moral authority and the teacher educator / Stefinee Pinnegar -- Conclusion. 15. Using a multi-linked conceptual framework to promote quality learning in a teacher education program / Garry F. …”

Tabla de Contenidos: “…Outlining the Nelson-Aalen Additive Model's Confidence Interval -- Discerning the Survival Hazard -- Discerning the Cumulative Survival Hazard -- Baseline Survival Hazard -- Conclusion -- Reference -- Chapter 8: Medical Records Categorization -- Medical Records -- Context of the Chapter -- Categorization with Linear Discriminant Analysis -- Descriptive Analysis -- Preprocessing the Medical Records Data -- Carrying Out a Regular Expression -- Carrying Out Word Vectorization -- Executing the Linear Discriminant Analysis Model to Classify Patients' Medical Records -- Considering the Linear Discriminant Analysis Model's Performance -- Conclusion -- Chapter 9: A Case for Psychology: Factoring and Clustering Personality Dimensions -- Personality Dimensions -- Questionnaires -- Likert Scale -- Scale Reliability -- Spearman-Brown Reliability Testing Strategy -- Carrying Out the Cronbach's Reliability Testing Strategy -- Carrying Out the Factor Model -- Carrying Out the Bartlett Sphericity Test -- Carrying Out the Kaiser-Meyer-Olkin Test -- Discerning K with a Scree Plot -- Carrying Out Eigenvalue Rotation -- Varimax Rotation -- Discerning Proportional Variance and Cumulative Variances -- Carrying Out Cluster Analysis -- Carrying Out Principal Component Analysis -- Returning K-Means Labels -- Discerning K-Means Cluster Centers -- Conclusion -- Index…”

Tabla de Contenidos: “…-- Linear regression and beyond -- Linear regression revisited for a real dataset -- Summary -- Chapter 3: Feed-Forward Neural Networks with TensorFlow -- Feed-forward neural networks (FFNNs) -- Feed-forward and backpropagation -- Weights and biases -- Activation functions -- Using sigmoid -- Using tanh -- Using ReLU -- Using softmax -- Implementing a feed-forward neural network -- Exploring the MNIST dataset -- Softmax classifier -- Implementing a multilayer perceptron (MLP) -- Training an MLP -- Using MLPs…”

Tabla de Contenidos: “…Multiple Univariate Models -- An Example: VAR for Forecasting Walmart Sales -- Key Takeaways -- Chapter 10: The VARMAX Model -- Model Definition -- Multiple Time Series with Exogenous Variables -- Key Takeaways -- Part IV: Supervised Machine Learning Models -- Chapter 11: The Linear Regression -- The Idea Behind Linear Regression -- Model Definition -- Example: Linear Model to Forecast CO2 Levels -- Key Takeaways -- Chapter 12: The Decision Tree Model -- Mathematics -- Splitting -- Pruning and Reducing Complexity -- Example -- Key Takeaways -- Chapter 13: The kNN Model -- Intuitive Explanation -- Mathematical Definition of Nearest Neighbors -- Combining k Neighbors into One Forecast -- Deciding on the Number of Neighbors k.…”

Tabla de Contenidos: “…Evaluating relations between variables with ANOVA -- Chapter 4: Dealing with Data and Numerical Issues -- Introduction -- Clipping and filtering outliers -- Winsorizing data -- Measuring central tendency of noisy data -- Normalizing with the Box-Cox transformation -- Transforming data with the power ladder -- Transforming data with logarithms -- Rebinning data -- Applying logit() to transform proportions -- Fitting a robust linear model -- Taking variance into account with weighted least squares -- Using arbitrary precision for optimization -- Using arbitrary precision for linear algebra -- Chapter 5: Web Mining, Databases, and Big Data -- Introduction -- Simulating web browsing -- Scraping the Web -- Dealing with non-ASCII text and HTML entities -- Implementing association tables -- Setting up database migration scripts -- Adding a table column to an existing table -- Adding indices after table creation -- Setting up a test web server -- Implementing a star schema with fact and dimension tables -- Using HDFS -- Setting up Spark -- Clustering data with Spark -- Chapter 6: Signal Processing and Timeseries -- Introduction -- Spectral analysis with periodograms -- Estimating power spectral density with the Welch method -- Analyzing peaks -- Measuring phase synchronization -- Exponential smoothing -- Evaluating smoothing -- Using the Lomb-Scargle periodogram -- Analyzing the frequency spectrum of audio -- Analyzing signals with the discrete cosine transform -- Block bootstrapping time series data -- Moving block bootstrapping time series data -- Applying the discrete wavelet transform -- Chapter 7: Selecting Stocks with Financial Data Analysis -- Introduction -- Computing simple and log returns -- Ranking stocks with the Sharpe ratio and liquidity -- Ranking stocks with the Calmar and Sortino ratios -- Analyzing returns statistics…”

Tabla de Contenidos: “…-- Different Types of Interpolation -- Linear Interpolation -- Polynomial Interpolation -- Piecewise Polynomial or Spline -- Nearest Neighbor Interpolation -- From One-Dimensional to Spatial Interpolation -- Spatial Interpolation in Python -- Linear Interpolation Using Scipy Interp2d -- Kriging -- Linear Ordinary Kriging -- Gaussian Ordinary Kriging -- Exponential Ordinary Kriging -- Conclusion on Interpolation Methods -- Key Takeaways -- Chapter 10: Classification -- Quick Intro to Machine Learning -- Quick Intro to Classification -- Spatial Classification Use Case -- Feature Engineering with Additional Data -- Importing and Inspecting the Data -- Spatial Operations for Feature Engineering -- Reorganizing and Standardizing the Data -- Modeling -- Model Benchmarking -- Key Takeaways -- Chapter 11: Regression -- Introduction to Regression -- Spatial Regression Use Case -- Importing and Preparing Data -- Iteration 1 of Data Exploration -- Iteration 1 of the Model -- Interpretation of Iteration 1 Model -- Iteration 2 of Data Exploration…”

Tabla de Contenidos: “…IF Subsampling -- Chapter 12: Error Correction Coding -- 12.1. Linear Block Encoding -- 12.2. Linear Block Decoding -- 12.3. …”

Tabla de Contenidos: “….) -- LA REFORMA/REVOLUCIÓN SOCIAL -- EL LAICISMO FEDERAL -- CULTURA FEDERAL VERSUS CULTURA LIBERAL POSREVOLUCIONARIA -- FRANCISCO GINER DE LOS RÍOS: KRAUSISMO, FILOSOFÍA Y POLÍTICA -- GINER DE LOS RÍOS Y LA TEORÍA POLÍTICA DEL KRAUSISMO -- REPÚBLICA O MONARQUÍA: ACCIDENTALIDAD DE LAS FORMAS DE GOBIERNO -- ORGANICISMO Y REPRESENTACIÓN: EL SUFRAGIO CORPORATIVO…”
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Tabla de Contenidos: “…Introduction -- 11.2. Linear Counting -- 11.2.1. Implemtation code of linear counting -- 11.3. …”

Tabla de Contenidos: “…Understanding cells -- Adding documentation cells -- Using other cell types -- Understanding the Use of Indentation -- Adding Comments -- Understanding comments -- Using comments to leave yourself reminders -- Using comments to keep code from executing -- Getting Help with the Python Language -- Working in the Cloud -- Using the Kaggle datasets and kernels -- Using the Google Colaboratory -- Chapter 4 Leveraging a Deep Learning Framework -- Presenting Frameworks -- Defining the differences -- Explaining the popularity of frameworks -- Defining the deep learning framework -- Choosing a particular framework -- Working with Low-End Frameworks -- Caffe2 -- Chainer -- PyTorch -- MXNet -- Microsoft Cognitive Toolkit/CNTK -- Understanding TensorFlow -- Grasping why TensorFlow is so good -- Making TensorFlow easier by using TFLearn -- Using Keras as the best simplifier -- Getting your copy of TensorFlow and Keras -- Fixing the C++ build tools error in Windows -- Accessing your new environment in Notebook -- Part 2 Considering Deep Learning Basics -- Chapter 5 Reviewing Matrix Math and Optimization -- Revealing the Math You Really Need -- Working with data -- Creating and operating with a matrix -- Understanding Scalar, Vector, and Matrix Operations -- Creating a matrix -- Performing matrix multiplication -- Executing advanced matrix operations -- Extending analysis to tensors -- Using vectorization effectively -- Interpreting Learning as Optimization -- Exploring cost functions -- Descending the error curve -- Learning the right direction -- Updating -- Chapter 6 Laying Linear Regression Foundations -- Combining Variables -- Working through simple linear regression -- Advancing to multiple linear regression -- Including gradient descent -- Seeing linear regression in action -- Mixing Variable Types -- Modeling the responses -- Modeling the features…”

Tabla de Contenidos: “…Unplanned comparisons -- Another Kind of Hypothesis, Another Kind of Test -- Working with repeated measures ANOVA -- Repeated measures ANOVA in R -- Visualizing the results -- Getting Trendy -- Trend Analysis in R -- Chapter 5 More Complicated Testing -- Cracking the Combinations -- Interactions -- The analysis -- Two-Way ANOVA in R -- Visualizing the two-way results -- Two Kinds of Variables . . . at Once -- Mixed ANOVA in R -- Visualizing the mixed ANOVA results -- After the Analysis -- Multivariate Analysis of Variance -- MANOVA in R -- Visualizing the MANOVA results -- After the MANOVA -- Chapter 6 Regression: Linear, Multiple, and the General Linear Model -- The Plot of Scatter -- Graphing Lines -- Regression: What a Line! …”

Tabla de Contenidos: “…Getting started -- The program -- The header -- Implementing Google's translation service -- Google Translate from a linguist's perspective -- Playing with the tool -- Linguistic assessment of Google Translate -- AI as a new frontier -- Lexical field and polysemy -- Exploring the frontier - customizing Google Translate with a Python program -- k-nearest neighbor algorithm -- Implementing the KNN algorithm -- The knn_polysemy.py program -- Implementing the KNN function in Google_Translate_Customized.py -- Conclusions on the Google Translate customized experiment -- The disruptive revolutionary loop -- Summary -- Questions -- Further reading -- Chapter 7: Optimizing Blockchains with Naive Bayes -- Part I - the background to blockchain technology -- Mining bitcoins -- Using cryptocurrency -- PART II - using blockchains to share information in a supply chain -- Using blockchains in the supply chain network -- Creating a block -- Exploring the blocks -- Part III - optimizing a supply chain with naive Bayes in a blockchain process -- A naive Bayes example -- The blockchain anticipation novelty -- The goal - optimizing storage levels using blockchain data -- Implementation of naive Bayes in Python -- Gaussian naive Bayes -- Summary -- Questions -- Further reading -- Chapter 8: Solving the XOR Problem with a Feedforward Neural Network -- The original perceptron could not solve the XOR function -- XOR and linearly separable models -- Linearly separable models -- The XOR limit of a linear model, such as the original perceptron -- Building an FNN from scratch -- Step 1 - defining an FNN -- Step 2 - an example of how two children can solve the XOR problem every day -- Implementing a vintage XOR solution in Python with an FNN and backpropagation -- A simplified version of a cost function and gradient descent -- Linear separability was achieved…”

Tabla de Contenidos: “…. -- Exercising greater control over nnet -- Generating raw probabilities and plotting the ROC curve -- Classifying using linear discriminant function analysis -- Getting ready -- How to do it... -- How it works... -- There's more…”

“…Creando mi profesión, pues, quiere ser una herramienta útil para generar y llevar a cabo procesos significativos de desarrollo profesional. …”
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Tabla de Contenidos: “…TUTORÍA DEL COMPLEMENTO TRANSBORDO.XLA -- OBJETIVO -- ENUNCIADO DEL PROBLEMA -- INCORPORACIÓN DEL PROBLEMA EN LA PLANILLA -- INGRESO DE LOS DATOS -- INGRESO DE INTERCAMBIOS NO PERMITIDOS -- DIBUJAR CASCADA -- GENERAR MODELO: MÍNIMO NÚMERO DE EQUIPOS -- RESOLVER EL MODELO -- ANEXO C. …”

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