Tabla de Contenidos: “…Data download and exploration -- Preparing the data for our models -- The binary classification model -- The regression model -- Improving the binary classification model -- The unreasonable effectiveness of data -- Summary -- Chapter 5: Image Classification Using Convolutional Neural Networks -- CNNs -- Convolutional layers -- Pooling layers -- Dropout -- Flatten layers, dense layers, and softmax -- Image classification using the MXNet library -- Base model (no convolutional layers) -- LeNet -- Classification using the fashion MNIST dataset -- References/further reading -- Summary -- Chapter 6: Tuning and Optimizing Models -- Evaluation metrics and evaluating performance -- Types of evaluation metric -- Evaluating performance -- Data preparation -- Different data distributions -- Data partition between training, test, and validation sets -- Standardization -- Data leakage -- Data augmentation -- Using data augmentation to increase the training data -- Test time augmentation -- Using data augmentation in deep learning libraries -- Tuning hyperparameters -- Grid search -- Random search -- Use case-using LIME for interpretability -- Model interpretability with LIME -- Summary -- Chapter 7: Natural Language Processing Using Deep Learning -- Document classification -- The Reuters dataset -- Traditional text classification -- Deep learning text classification -- Word vectors -- Comparing traditional text classification and deep learning -- Advanced deep learning text classification -- 1D convolutional neural network model -- Recurrent neural network model -- Long short term memory model -- Gated Recurrent Units model -- Bidirectional LSTM model -- Stacked bidirectional model -- Bidirectional with 1D convolutional neural network model -- Comparing the deep learning NLP architectures -- Summary -- Chapter 8: Deep Learning Models Using TensorFlow in R.…”

Datos de acceso al recurso electrónico
Acceso a la plataforma Legalteca, busque el título una vez dentro.

Tabla de Contenidos: “…Voting classifier -- Summary -- Chapter 9: Clustering Fundamentals -- Clustering basics -- k-NN -- Gaussian mixture -- Finding the optimal number of components -- K-means -- Finding the optimal number of clusters -- Optimizing the inertia -- Silhouette score -- Calinski-Harabasz index -- Cluster instability -- Evaluation methods based on the ground truth -- Homogeneity -- Completeness -- Adjusted Rand Index -- Summary -- Chapter 10: Advanced Clustering -- DBSCAN -- Spectral Clustering -- Online Clustering -- Mini-batch K-means -- BIRCH -- Biclustering -- Summary -- Chapter 11: Hierarchical Clustering -- Hierarchical strategies -- Agglomerative Clustering -- Dendrograms -- Agglomerative Clustering in scikit-learn -- Connectivity constraints -- Summary -- Chapter 12: Introducing Recommendation Systems -- Naive user-based systems -- Implementing a user-based system with scikit-learn -- Content-based systems -- Model-free (or memory-based) collaborative filtering -- Model-based collaborative filtering -- Singular value decomposition strategy -- Alternating least squares strategy -- ALS with Apache Spark MLlib -- Summary -- Chapter 13: Introducing Natural Language Processing -- NLTK and built-in corpora -- Corpora examples -- The Bag-of-Words strategy -- Tokenizing -- Sentence tokenizing -- Word tokenizing -- Stopword removal -- Language detection -- Stemming -- Vectorizing -- Count vectorizing -- N-grams -- TF-IDF vectorizing -- Part-of-Speech -- Named Entity Recognition -- A sample text classifier based on the Reuters corpus -- Summary -- Chapter 14: Topic Modeling and Sentiment Analysis in NLP -- Topic modeling -- Latent Semantic Analysis -- Probabilistic Latent Semantic Analysis -- Latent Dirichlet Allocation -- Introducing Word2vec with Gensim -- Sentiment analysis -- VADER sentiment analysis with NLTK -- Summary…”

Actualització

Tabla de Contenidos: “…. -- Effect of the Addition of Agribusiness and Industrial Wastes as a Partial Substitution of Portland -- Cement for the Carbonation of Mortars -- Reprinted from: Materials 2021, 14, 7276, doi:10.3390/ma14237276 103 -- Banu Sizirici, Yohanna Fseha, Chung-Suk Cho, Ibrahim Yildiz and Young-Ji Byon -- A Review of Carbon Footprint Reduction in Construction Industry, from Design to Operation -- Reprinted from: Materials 2021, 14, 6094, doi:10.3390/ma14206094 125 -- Miyeon Kim, Hyunggeun Kim and Jinchul Park -- Empirical NOx Removal Analysis of Photocatalytic Construction Materials at Real-Scale -- Reprinted from: Materials 2021, 14, 5717, doi:10.3390/ma14195717 143 -- Andreja Padovnik and Violeta Bokan-Bosiljkov -- The Influence of Dry Hydrated Limes on the Fresh and Hardened Properties of Architectural -- Injection Grout -- Reprinted from: Materials 2021, 14, 5585, doi:10.3390/ma14195585 161 -- v -- Hyun-Sub Yoon, Keun-Hyeok Yang, Kwang-Myong Lee and Seung-Jun Kwon -- Service Life Evaluation for RC Sewer Structure Repaired with Bacteria Mixed Coating: Through -- Probabilistic and Deterministic Method -- Reprinted from: Materials 2021, 14, 5424, doi:10.3390/ma14185424 177 -- Ehsan Nasseralshariati, Danial Mohammadzadeh, Nader Karballaeezadeh, Amir Mosavi, -- Uwe Reuter and Murat Saatcioglu -- The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of -- Concrete -- Reprinted from: Materials 2021, 14, 4088, doi:10.3390/ma14154088 197 -- Mei-Yu Xuan, Yi-Sheng Wang, Xiao-Yong Wang, Han-Seung Lee and Seung-Jun Kwon -- Effect of Cement Types and Superabsorbent Polymers on the Properties of Sustainable -- Ultra-High-Performance Paste -- Reprinted from: Materials 2021, 14, 1497, doi:10.3390/ma14061497 221 -- Dongho Jeon, Woo Sung Yum, Haemin Song, Seyoon Yoon, Younghoon Bae and Jae Eun Oh -- Use of Coal Bottom Ash and CaO-CaCl2-Activated GGBFS Binder in the Manufacturing of -- Artificial Fine Aggregates through Cold-Bonded Pelletization -- Reprinted from: Materials 2020, 13, 5598, doi:10.3390/ma13245598 241 -- Sun-Jae Yoo, Tian-Feng Yuan, Se-Hee Hong and Young-Soo Yoon -- Effect of Strengthening Methods on Two-Way Slab under Low-Velocity Impact Loading -- Reprinted from: Materials 2020, 13, 5603, doi:10.3390/ma13245603 259 -- Junxing Liu, Hyeonseok Jee, Myungkwan Lim, Joo Hyung Kim, Seung Jun Kwon and -- Kwang Myong Lee et al. -- Photocatalytic Performance Evaluation of Titanium Dioxide Nanotube-Reinforced -- Cement Paste -- Reprinted from: Materials 2020, 13, 5423, doi:10.3390/ma13235423 275 -- Rizwan Hameed, Joonho Seo, Solmoi Park, Issam T. …”

“…Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. …”

Tabla de Contenidos: “…The Communication Process: An Overview -- Rural Marketing Insight: Rural and Urban Responses to Television Advertisements -- Developing Effective Rural Communication -- Identifying and Profiling the Target Audience -- Determining Communication Objectives -- Designing the Message -- Selecting the Communication Channels -- Budgeting for Rural Communication -- Designing the Communication Mix Strategy Companies Face the Task of Distributing the Total Communication -- Measuring the Impact of Communication -- Rural Marketing Insight: Religion: A Key Profiling Factor for Designing Communication -- Rural Marketing Memo: Developing an Effective Rural Communication Message -- Creating Advertisements for Rural Audiences -- Rural Marketing Insight: Hitting the Bull's Eye -- Rural Media -- Conventional Media -- Rural-centric Non-conventional Media -- Rural Marketing Insight: Increasing Role of DTH in Rural India -- Rural Marketing Snapshot: Changing Means of Rural Communication -- Rural Marketing Insight: Clutter-free Communication at the Mela -- Sales Promotion and Events and Experiences -- Sales Promotion -- Events and Experiences -- Review of Objectives -- Discussion and Application -- Rural Marketing Case: Tata Shaktee GC Sheets -- Chapter 10: Rural Services Marketing -- Telecommunications in Rural India -- Reuters Market Light -- IFFCO Kisaan Sanchar Limited -- Nokia Life Tools -- Rural Marketing Snapshot: The Telecom Revolution in Rural india -- Rural Marketing Insight: M-VAS Initiatives in Rural India -- Information and Communications Technology (ICT) in Rural Areas -- ITC's e-choupal -- Common Service Centres -- n-Logue Village Internet Kiosks -- Drishtee -- The Relevance of ICT Services in Rural India -- Financial Services in Rural India -- Banking Services -- Microfinance and Credit Services -- Insurance -- Life Insurance…”

Tabla de Contenidos: “….) -- 2.1.2 - The OECD (at: stats.oecd.org) -- 2.1.3 - Eurostat and other country statistical agencies (at: ec.europa.eu/eurostat/data/database) -- 2.1.4 - International Energy Agency (at: www.eia.gov/tools) -- 2.1.5 - British Petroleum data (at: www.bp.com) -- 2.1.6 - Thomson Reuters Datastream (at: financial.thomsonreuters.com) -- 2.1.7 - ODYSSE and MURE database (at: indicators.odyssee-mure.eu) -- 2.1.8 - Penn World Tables (PWT) (at: cid.econ.ucdavis.edu) -- 2.1.9 - "Data in Brief" Journal by Elsevier -- 2.1.10 - Database of Political Institutions (DPI) (at: econ.worldbank.org) -- 2.1.11 - Various micro data -- 2.2 - Data Type and Frequency in the Energy-Growth Nexus -- Case study 1: daily data -- Case study 2: weekly data…”

Tabla de Contenidos: “…Chapter 4 Feel the Love: Welcoming Commodities into Your Portfolio -- The Color of Money: Taking Control of Your Financial Life -- Looking Ahead: Creating a Financial Road Map -- Figuring out your net worth -- Identifying your tax bracket -- Determining your appetite for risk -- Making Room in Your Portfolio for Commodities -- Fully Exposed: The Top Ways to Get Exposure to Commodities -- Looking toward the future with commodity futures -- Commodity index -- Futures commission merchant -- Commodity trading advisor -- Commodity pool operator -- Funding your account with commodity funds -- Commodity mutual funds -- Exchange-traded funds -- You're in good company: Investing in commodity companies -- Publicly traded companies -- Master limited partnerships -- Part 2 Getting Started with Types of Investment Vehicles -- Chapter 5 Benefiting from Exchange-Traded Funds -- Getting to Know ETFs -- Accessing a Variety of Commodity Markets through ETFs -- Taking a Look at Leveraged ETFs -- Chapter 6 Making a Splash: Investing in Liquid Commodity Instruments -- Checking Out Commodity Indexes -- Why indexes are useful -- How to make money by using an index -- From Head to Toe: Uncovering the Anatomy of a Commodity Index -- Cataloguing the Five Major Indexes -- The S&amp -- P Goldman Sachs Commodity Index -- Reuters/Jefferies Commodity Research Bureau Index -- Dow Jones Commodity Index -- Rogers International Commodities Index -- Deutsche Bank Liquid Commodity Index -- Determining Which Index to Use -- Chapter 7 Show Me the Money! …”

Tabla de Contenidos: “…-- 2.4.2 Ableitung einer Tensoroperation: der Gradient -- 2.4.3 Stochastisches Gradientenabstiegsverfahren -- 2.4.4 Ableitungen verketten: der Backpropagation-Algorithmus -- 2.5 Zurück zum ersten Beispiel -- 2.6 Zusammenfassung Kapitel 2 -- Kapitel 3: Einführung in neuronale Netze -- 3.1 Aufbau eines neuronalen Netzes -- 3.1.1 Layer: Bausteine des Deep Learnings -- 3.1.2 Modelle: vernetzte Layer -- 3.1.3 Verlustfunktionen und Optimierer: Konfiguration des Lernvorgangs -- 3.2 Einführung in Keras -- 3.2.1 Keras, TensorFlow, Theano und CNTK -- 3.2.2 Keras installieren -- 3.2.3 Mit Keras entwickeln: eine kurze Übersicht -- 3.3 Einrichtung eines Deep-Learning-Rechners -- 3.3.1 Zwei Möglichkeiten, Keras zum Laufen zu bringen -- 3.3.2 Pro und Kontra: Deep Learning in der Cloud ausführen -- 3.3.3 Für Deep Learning geeignete GPUs -- 3.4 Klassifikation von Filmbewertungen: ein Beispiel für eine Binärklassifikation -- 3.4.1 Die IMDb-Datensammlung -- 3.4.2 Daten vorbereiten -- 3.4.3 Neuronales Netz erzeugen -- 3.4.4 Validierung des Ansatzes -- 3.4.5 Vorhersagen über neue Daten mit einem trainierten neuronalen Netz treffen -- 3.4.6 Weitere Experimente -- 3.4.7 Zusammenfassung -- 3.5 Ein Beispiel für eine Mehrfachklassifikation: Klassifikation von Nachrichtenmeldungen -- 3.5.1 Die Reuters-Datensammlung -- 3.5.2 Daten vorbereiten -- 3.5.3 Neuronales Netz erzeugen -- 3.5.4 Validierung des Ansatzes -- 3.5.5 Vorhersagen über neue Daten treffen…”

Tabla de Contenidos: “…Classifying newswires: a multiclass classification example -- 3.5.1. The Reuters dataset -- 3.5.2. Preparing the data -- 3.5.3. …”