Tabla de Contenidos: “…Ajasson de Grandsagne -- Pars sexta continens mineralogiam / curante Delafosse -- Tres indices locupletissimi : primus verba et sententias, secundus geographiam, tertius nomina propria exhibet / curante Angelo Pihan de la Forest…”

Tabla de Contenidos: “…Medieval hunting / An Smets and Baudouin van den Abeele -- 3. From forest to farm and town : domestic animals from ca. 1000 to ca. 1450 / Esther Pascua -- 4. …”

Tabla de Contenidos: “…Predicting Traffic Using kNN -- Grid Search on kNN -- Random Search: An Alternative to Grid Search -- Key Takeaways -- Chapter 14: The Random Forest -- Intuitive Idea Behind Random Forests -- Random Forest Concept 1: Ensemble Learning -- Bagging Concept 1: Bootstrap -- Bagging Concept 2: Aggregation -- Random Forest Concept 2: Variable Subsets -- Predicting Sunspots Using a Random Forest -- Grid Search on the Two Main Hyperparameters of the Random Forest -- Random Search CV Using Distributions -- Distribution for max_features -- Distribution for n_estimators -- Fitting the RandomizedSearchCV -- Interpretation of Random Forests: Feature Importance -- Key Takeaways -- Chapter 15: Gradient Boosting with XGBoost and LightGBM -- Boosting: A Different Way of Ensemble Learning -- The Gradient in Gradient Boosting -- Gradient Boosting Algorithms -- The Difference Between XGBoost and LightGBM -- Forecasting Traffic Volume with XGBoost -- Forecasting Traffic Volume with LightGBM -- Hyperparameter Tuning Using Bayesian Optimization -- The Theory of Bayesian Optimization -- Bayesian Optimization Using scikit-optimize -- Conclusion -- Key Takeaways -- Part V: Advanced Machine and Deep Learning Models -- Chapter 16: Neural Networks -- Fully Connected Neural Networks -- Activation Functions -- The Weights: Backpropagation -- Optimizers -- Learning Rate of the Optimizer -- Hyperparameters at Play in Developing a NN -- Introducing the Example Data -- Specific Data Prep Needs for a NN -- Scaling and Standardization -- Principal Component Analysis (PCA) -- The Neural Network Using Keras -- Conclusion -- Key Takeaways -- Chapter 17: RNNs Using SimpleRNN and GRU -- What Are RNNs: Architecture -- Inside the SimpleRNN Unit -- The Example -- Predicting a Sequence Rather Than a Value -- Univariate Model Rather Than Multivariable -- Preparing the Data -- A Simple SimpleRNN…”

Tabla de Contenidos: “…Example of logistic regression using German credit data -- Random forest -- Example of random forest using German credit data -- Grid search on random forest -- Variable importance plot -- Comparison of logistic regression with random forest -- Summary -- Chapter 4: Tree-Based Machine Learning Models -- Introducing decision tree classifiers -- Terminology used in decision trees -- Decision tree working methodology from first principles -- Comparison between logistic regression and decision trees -- Comparison of error components across various styles of models -- Remedial actions to push the model towards the ideal region -- HR attrition data example -- Decision tree classifier -- Tuning class weights in decision tree classifier -- Bagging classifier -- Random forest classifier -- Random forest classifier - grid search -- AdaBoost classifier -- Gradient boosting classifier -- Comparison between AdaBoosting versus gradient boosting -- Extreme gradient boosting - XGBoost classifier -- Ensemble of ensembles - model stacking -- Ensemble of ensembles with different types of classifiers -- Ensemble of ensembles with bootstrap samples using a single type of classifier -- Summary -- Chapter 5: K-Nearest Neighbors and Naive Bayes -- K-nearest neighbors -- KNN voter example -- Curse of dimensionality -- Curse of dimensionality with 1D, 2D, and 3D example -- KNN classifier with breast cancer Wisconsin data example -- Tuning of k-value in KNN classifier -- Naive Bayes -- Probability fundamentals -- Joint probability -- Understanding Bayes theorem with conditional probability -- Naive Bayes classification -- Laplace estimator -- Naive Bayes SMS spam classification example -- Summary -- Chapter 6: Support Vector Machines and Neural Networks -- Support vector machines working principles -- Maximum margin classifier -- Support vector classifier…”

Tabla de Contenidos: “…Cover -- Contents -- Preface -- Chapter 1: The Multi-disciplinary Nature of Environmental Education -- Environment: Definition and Scope -- Introduction -- Environment Explained -- Definitions of Environment -- Scope of Environment -- Elements of the Environment -- Environmental Education: Importance -- Importance of Environmental Education -- Need for Public Awareness -- Various Types of Environment -- Structure of Environment -- Questions -- Chapter 2: Nature Resources: Renewable and Non-renewable Resources -- Resources-Meaning and Types -- Definition of Natural Resources -- Renewable and Non-renewable Resources -- Forest Resources -- Importance of Forest Resources -- State Percentage of Forests Area -- Distribution of Forests -- Overconsumption of Forests Reasons -- Deforestation: Meaning and Results -- Meaning of Deforestation -- Formidable Picture of Deforestation -- Evil Consequences of Deforestation -- Water Resources: Use and Over-utilization of Surface and Groundwater -- Significance of Water -- Surface Flow -- River -- Lakes and Ponds -- Lotic Eco-systems or Moving Water -- Estuaries -- Water Resource Management -- Measures to Check Over-Utilization of Water Resources -- Regulation of Groundwater Utilization and Restricting its Over-utilization -- Various Steps -- Flood Management -- National Flood Control Programme -- Measures and Achievements -- Forecasting -- Thrust Area During the Eighth-plan Period -- Ganga Flood Control Commission -- Drought-prone Areas Programme -- Desert Development Programme -- Inter-state Water Disputes -- Reasons for Differences -- Effort to Resolve Disputes -- Boards and Committees -- International Agreements -- Indo-Nepal Sub-commission on Water Resources -- Dams: Description, Benefits and Problems -- Farakka Barrage Project -- Various Dams in the Country and Benefits the Refrom…”

Tabla de Contenidos: “…-- Part 3: Machine learning -- Tools and data for machine-learning projects -- Decisions, decisions, decisions -- Into the forest, randomly -- Support your local vector -- K-means clustering -- Neural networks -- Part 4: Large(ish) data sets -- Exploring marketing -- From the city that never sleeps -- Part 5: Maps and images -- All over the map -- Fun with pictures -- Part 6: The part of tens -- More than ten packages for your R-projects -- More than ten useful resources…”

Tabla de Contenidos: “…Cover; MCSE 70-294 Exam Objectives Map; Contents; Foreword; Chapter 1 Active Directory Infrastructure Overview; Chapter 2 Working with User, Group, and Computer Accounts; Chapter 3 Creating User and Group Strategies; Chapter 4 Working with Forests and Domains; Chapter 5 Working with Trusts and Organizational Units; Chapter 6 Working with Active Directory Sites; Chapter 7 Working with Domain Controllers; Chapter 8 Working with Global Catalog Servers and Schema; Chapter 9 Working with Group Policy in an Active Directory Environment; Chapter 10 Deploying Software via Group Policy…”

Tabla de Contenidos: “…Byrnes -- Of gardens, forests, and parks / Peter J. Katzenstein.s -- Index…”

Tabla de Contenidos: “…The (new) cinematic zombie: road trips, globalization and World War Z -- The comedic zombie: zombieland and the classical functions of the modern zomedy -- The young adult zombie: teenage anxiety in The forest of hands and teeth -- Beyond film. The comic book zombie: human devolution in the walking dead -- The literary zombie: the infected city of Colson Whitehead's Zone one -- The stage zombie: Dead set, Uncle Vanya and zombies and the reality-tv monster -- Broader horizons. …”

Tabla de Contenidos: “…The Machine Learning Landscape -- End-to-End Machine Learning Project -- Classification -- Training Models -- Support Vector Machines -- Decision Trees -- Ensemble Learning and Random Forests -- Dimensionality Reduction -- Unsupervised Learning Techniques – Part II. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributor -- Table of Contents -- Preface -- Chapter 1: Ensemble Methods for Regression and Classification -- Ensemble methods and their working -- Bootstrap sampling -- Bagging -- Random forests -- Boosting -- Ensemble methods for regression -- The diamond dataset -- Training different regression models -- KNN model -- Bagging model -- Random forests model -- Boosting model -- Using ensemble methods for classification -- Predicting a credit card dataset -- Training different regression models -- Logistic regression model -- Bagging model -- Random forest model -- Boosting model -- Summary -- Chapter 2: Cross-validation and Parameter Tuning -- Holdout cross-validation -- K-fold cross-validation -- Implementing k-fold cross-validation -- Comparing models with k-fold cross-validation -- Introduction to hyperparameter tuning -- Exhaustive grid search -- Hyperparameter tuning in scikit-learn -- Comparing tuned and untuned models -- Summary -- Chapter 3: Working with Features -- Feature selection methods -- Removing dummy features with low variance -- Identifying important features statistically -- Recursive feature elimination -- Dimensionality reduction and PCA -- Feature engineering -- Creating new features -- Improving models with feature engineering -- Training your model -- Reducible and irreducible error -- Summary -- Chapter 4: Introduction to Artificial Neural Networks and TensorFlow -- Introduction to ANNs -- Perceptrons -- Multilayer perceptron -- Elements of a deep neural network model -- Deep learning -- Elements of an MLP model -- Introduction to TensorFlow -- TensorFlow installation -- Core concepts in TensorFlow -- Tensors -- Computational graph -- Summary -- Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks -- Predictions with TensorFlow…”

Tabla de Contenidos: “…The LogisticRegressionParameter and CsvRecordFactory classCode example without the parameter class; Testing the online regression model; Getting predictions from OnlineLogisticRegression; CrossFoldLearner example; Random forest; Bagging; Random subsets of features; Out-of-bag error estimate; Random forest using the command line; Predictions from random forest; Naïve Bayes classifier; Numeric features with naïve Bayes; Command line; Summary; Chapter 5: Frequent Pattern Mining and Topic Modeling; Frequent pattern mining; Building FP Tree; Constructing the tree…”

Tabla de Contenidos: “…Analyzing Naming Convention NeedsTrees and Forests; Defining a Naming Convention; Determining Name Resolution; Planning a Domain Structure; Domains vs. …”

Tabla de Contenidos: “…Creating and Configuring Application Data PartitionsConfiguring DNS Integration with Active Directory; Summary; Exam Essentials; Review Questions; Answers to Review Questions; Chapter 4: Installing and Managing Trees and Forests; Reasons for Creating Multiple Domains; Creating Domain Trees and Forests; Demoting a Domain Controller; Managing Multiple Domains; Summary; Exam Essentials; Review Questions; Answers to Review Questions; Chapter 5: Configuring Sites and Replication; Overview of Network Planning; Overview of Active Directory Replication and Sites; Implementing Sites and Subnets…”

Tabla de Contenidos:

Tabla de Contenidos: “….); PÁGINA LEGAL; ÍNDICE; PRÓLOGO; CAPÍTULO 1 UNA FAMILIA COMO YA NO HAY TANTAS; CON LA BENDICIÓN DE LA MADRE; LA PROFUNDIDAD DE LOS CIMIENTOS; ULTIMO RECORRIDO EN PALACIO NACIONAL; EL PRIMER DÍA DE MUCHOS MÁS; GENERACIÓN EN TURNO: LOS PANDAL PÉREZ; CAPÍTULO 2 UN TIEMPO QUE NO SE REPETIRÁ; PANIXTLAHUACA: SUEÑOS Y ENSUEÑOS; LA VOCACIÓN FORESTAL: INDUSTRIA Y EMPRESA; DE LA CÁMARA DE LA INDUSTRIA FORESTAL A (...); EN LA CUMBRE DE LOS INDUSTRIALES; COSTUMBRES PRIVADAS, VIDA PÚBLICA; CAPÍTULO 3 EL DÍA QUE LA CONCIENCIA SE (...); DESPERTARES…”

Tabla de Contenidos: “…Amanogawa bridge (Tokachi Millenium Forest, Hokkaido, Japan) / Rintala Eggertsson Architects…”

Tabla de Contenidos: “…Developing the Active Directory Infrastructure Designs; Introduction; Assessing and Designing the Administrative Model; Assessing and Defining the Forest Design; Assessing and Creating a Domain Design; Developing the OU Model…”

Tabla de Contenidos: “…Geographic expertise and citizen science: planning and co-design implications ; Citizen science mobile apps for soundscape research and public space studies: lessons from the Hush City project ; Using mixed methods to enhance user experience: developing Global Forest Watch ; Path of least resistance: using geo-games and crowdsourced data to map cycling frictions ; Geographic citizen science in citizen-government communication and collaboration: lessons learned from the ImproveMyCity application -- Part III, Geographic citizen science with indigenous communities. …”

Tabla de Contenidos: “…CAPÍTULO 16. ROTACIÓN FORESTAL ÓPTIMACAPÍTULO 17. GESTIÓN PESQUERA…”