Materias: “…Karmi-Melamede, Ada…”

Tabla de Contenidos: “…Breeding tables / Clemens Weisshaar & Reed Kram. IVY / Michael Meredith, mos. 30 St. Mary Axe, Edificio Swiss Re / Foster + Partners. …”

Tabla de Contenidos: “…Och Moder, ich well en Ding han / Brahms, popular de Colonia. Mausfallen-Sprüchlein / Wolf, Mörike -- Cara B. …”

Tabla de Contenidos: “…stretch, shrink, warp, and rotate""; ""Discrete Fourier Transform and Discrete Cosine Transform""; ""Integral images""; ""Distance transforms""; ""Histogram equalization""; ""References""; ""Summary""; ""Chapter 5: Object Detection Using Ada Boost and Haar Cascades""; ""Boosting theory""; ""AdaBoost""; ""Cascade classifier detection and training""; ""Detection""…”

Enlace del recurso

Tabla de Contenidos: “…UNA LECTURA DE PASCOLI ACERCA DE LOS DESTINOS ÚLTIMOS; 3. EL DRAMA DE CLEMENTE REBORA; 4. EL PROBLEMA DE LA CONVERSIÓN EN ADA NEGRI; 5. …”

Tabla de Contenidos: “…Simmons 10 Deep Pasts Interconnections and Comparative History in the Ancient World Norman Yoffee 11 Big History Fred Spier 12 Global Scale Analysis in Human History Christopher Chase-Dunn and Thomas D Hall 13 Region in Global History Paul A.Kramer 14 Scales of a Local: The Place of Locality in a Globalizing World Anne Gerritsen Comparing 15 Comparative History and the Challenge of the Grand Narrative Michael Adas 16 The Science of Difference: Race, Indo-European Linguistics, and Eurasian Nomads Xinru Liu 17 Projecting power: empires, colonies, and world history Mrinalini Sinha 18 The Body in/as World History Antoinette Burton 19 Benchmarks of Globalization: the Global Condition, 1850-2010 Charles Bright and Michael Geyer Connecting 20 Networks, Interactions, and Connective History Felipe Fernandez-Armesto with Benjamin Sacks 21 Objects in Motion: The Long History of Globalization Scott C. …”

Tabla de Contenidos: “…s Reliefs""; ""Akkadian and Aramaic Terms for a â€?Favorable Timeâ€? (á?«idÄ?nu, adÄ?…”
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Tabla de Contenidos: “…From a standalone machine to a bunch of nodes -- Making sense of why we need a distributed framework -- The Hadoop ecosystem -- Hadoop architecture -- Hadoop Distributed File System -- MapReduce -- Introducing Apache Spark -- PySpark -- Starting with PySpark -- Setting up your local Spark instance -- Experimenting with Resilient Distributed Datasets -- Sharing variables across cluster nodes -- Read-only broadcast variables -- Write-only accumulator variables -- Broadcast and accumulator variables together-an example -- Data preprocessing in Spark -- CSV files and Spark DataFrames -- Dealing with missing data -- Grouping and creating tables in-memory -- Writing the preprocessed DataFrame or RDD to disk -- Working with Spark DataFrames -- Machine learning with Spark -- Spark on the KDD99 dataset -- Reading the dataset -- Feature engineering -- Training a learner -- Evaluating a learner's performance -- The power of the machine learning pipeline -- Manual tuning -- Cross-validation -- Final cleanup -- Summary -- Appendix: Strengthen Your Python Foundations -- Your learning list -- Lists -- Dictionaries -- Defining functions -- Classes, objects, and object-oriented programming -- Exceptions -- Iterators and generators -- Conditionals -- Comprehensions for lists and dictionaries -- Learn by watching, reading, and doing -- Massive open online courses (MOOCs) -- PyCon and PyData -- Interactive Jupyter -- Don't be shy, take a real challenge -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…8.2 Optimierung: Lernen, um die Kosten zu minimieren -- 8.2.1 Der Gradientenabstieg -- 8.2.2 Die Lernrate -- 8.2.3 Batch-Größe und stochastischer Gradientenabstieg -- Trainingsrunde: -- 8.2.4 Dem lokalen Minimum entkommen -- 8.3 Backpropagation -- 8.4 Die Anzahl der verborgenen Schichten und der Neuronen anpassen -- 8.5 Ein mittleres Netz in Keras -- 8.6 Zusammenfassung -- Schlüsselkonzepte -- 9 Deep Networks verbessern -- 9.1 Die Initialisierung der Gewichte -- 9.1.1 Xavier-Glorot-Verteilungen -- 9.2 Instabile Gradienten -- 9.2.1 Verschwindende Gradienten -- 9.2.2 Explodierende Gradienten -- 9.2.3 Batch-Normalisierung -- 9.3 Modellgeneralisierung (Überanpassung vermeiden) -- 9.3.1 L1- und L2-Regularisierung -- 9.3.2 Dropout -- 9.3.3 Datenaugmentation -- 9.4 Intelligente Optimierer -- 9.4.1 Momentum -- 9.4.2 Nesterov-Momentum -- 9.4.3 AdaGrad -- 9.4.4 AdaDelta und RMSProp -- 9.4.5 Adam -- 9.5 Ein tiefes neuronales Netz in Keras -- 9.6 Regression -- 9.7 TensorBoard -- 9.8 Zusammenfassung -- Schlüsselkonzepte -- Teil III -- Interaktive Anwendungen des Deep Learning -- 10 Maschinelles Sehen -- 10.1 Convolutional Neural Networks -- 10.1.1 Die zweidimensionale Struktur der visuellen Bilddarstellung -- 10.1.2 Berechnungskomplexität -- 10.1.3 Konvolutionsschichten -- 10.1.4 Mehrere Filter -- 10.1.5 Ein Beispiel für Konvolutionsschichten -- 10.2 Hyperparameter von Konvolutionsfiltern -- 10.2.1 Kernel-Größe -- 10.2.2 Schrittlänge -- 10.2.3 Padding -- 10.3 Pooling-Schichten -- 10.4 LeNet-5 in Keras -- 10.5 AlexNet und VGGNet in Keras -- 10.6 Residualnetzwerke -- 10.6.1 Schwindende Gradienten: Das Grauen der tiefen CNN -- 10.6.2 Residualverbindungen -- 10.6.3 ResNet -- 10.7 Anwendungen des maschinellen Sehens -- 10.7.1 Objekterkennung -- R-CNN -- Fast R-CNN -- Faster R-CNN -- YOLO -- 10.7.2 Bildsegmentierung -- Mask R-CNN -- U-Net -- 10.7.3 Transfer-Lernen…”

Tabla de Contenidos: “…3.3 Methodology of Designing Quantum Multiplexer (QMUX) -- 3.3.1 QMUX Using CSWAP Gates -- 3.3.1.1 Generalization -- 3.3.2 QMUX Using Controlled-R Gates -- 3.4 Analysis and Synthesis of Proposed Methodology -- 3.5 Complexity and Cost of Quantum Circuits -- 3.6 Conclusion -- References -- Chapter 4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain -- 4.1 Introduction -- 4.1.1 Quantum Computing Convolutional Neural Network -- 4.2 Literature Survey -- 4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems -- 4.3.1 Minimizing Quantum Algorithm -- 4.3.2 K-NN Algorithm -- 4.3.3 K-Means Algorithm -- 4.4 Tree Tensor Networking -- 4.5 TNN Implementation on IBM Quantum Processor -- 4.6 Neurotomography -- 4.7 Conclusion and Future Scope -- References -- Chapter 5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Work -- 5.3.1 Methodology -- 5.3.2 Work Flow of Neural Style Transfer -- 5.3.3 A-Frame -- 5.3.3.1 Setting Up the Virtual World and Adding Components -- 5.3.3.2 Adding Interactivity Through Raycasting -- 5.3.3.3 Animating the Components -- 5.3.4 Neural Style Transfer -- 5.3.4.1 Choosing the Content and Styling Image -- 5.3.4.2 Image Preprocessing and Generation of a Random Image -- 5.3.4.3 Model Design and Extraction of Content and Style -- 5.3.4.4 Loss Calculation -- 5.3.4.5 Model Optimization -- 5.4 Evaluation Metrics -- 5.5 Results -- 5.5.1 A-Frame -- 5.5.2 Neural Style Transfer -- 5.6 Conclusion -- References -- Chapter 6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Model -- 6.3.1 URL Feature Extractor -- 6.3.2 Dataset -- 6.3.3 Methodologies -- 6.3.3.1 AdaBoost Classifier…”

Tabla de Contenidos: “…Implementing a macro using the Jinja2 templating engine -- Adding style via CSS -- Creating the result page -- Turning the movie review classifier into a web application -- Files and folders - looking at the directory tree -- Implementing the main application as app.py -- Setting up the review form -- Creating a results page template -- Deploying the web application to a public server -- Creating a PythonAnywhere account -- Uploading the movie classifier application -- Updating the movie classifier -- Summary -- Chapter 10: Predicting Continuous Target Variables with Regression Analysis -- Introducing linear regression -- Simple linear regression -- Multiple linear regression -- Exploring the Housing dataset -- Loading the Housing dataset into a data frame -- Visualizing the important characteristics of a dataset -- Looking at relationships using a correlation matrix -- Implementing an ordinary least squares linear regression model -- Solving regression for regression parameters with gradient descent -- Estimating coefficient of a regression model via scikit-learn -- Fitting a robust regression model using RANSAC -- Evaluating the performance of linear regression models -- Using regularized methods for regression -- Turning a linear regression model into a curve - polynomial regression -- Adding polynomial terms using scikit-learn -- Modeling nonlinear relationships in the Housing dataset -- Dealing with nonlinear relationships using random forests -- Decision tree regression -- Random forest regression -- Summary -- Chapter 11: Working with Unlabeled Data - Clustering Analysis -- Grouping objects by similarity using k-means -- K-means clustering using scikit-learn -- A smarter way of placing the initial cluster centroids using k-means++ -- Hard versus soft clustering -- Using the elbow method to find the optimal number of clusters…”