Tabla de Contenidos: “…-- 4.3.3 Sign Test -- 4.3.4 Paired t‐Test -- 4.3.4.1 Effect of Violating Normality Assumption in Paired t‐Test -- 4.3.5 Rank Test -- 4.3.6 Independent Two‐Sample t‐Test -- 4.3.6.1 Two‐Sample t‐Test with SPSS and Testing Assumptions -- 4.3.6.2 Effect of Violating Assumption of Homogeneity -- 4.4 F‐test For Comparing Variability -- 4.4.1 Analysis of Variance (ANOVA) -- 4.4.2 ANOVA Assumptions -- 4.4.2.1 Checking Assumptions Using SPSS -- 4.4.3 One‐Way ANOVA Using SPSS -- 4.4.4 What to Do if Assumption Violates? …”

Tabla de Contenidos: “…B.3 Overloading Details -- Bibliography -- Newsgroups -- Books and Web Sites -- Glossary -- A -- C -- D -- E -- F -- H -- I -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- W -- Index…”

Tabla de Contenidos:

Tabla de Contenidos: “…-- Brand associations and sales promotion/pricediscounting -- Perceived Risk and Branding -- Product category and perceived risk -- New concept products -- Diffusing durable categories -- Intention to buy matters -- The Power of Brand Associations -- Importance of brand associations -- New brands and brand associations -- Importance of brand associations for pioneeringbrands -- The Enigma of the Three C's-Culture, Cricket and Consumers -- The beginnings -- Consumer behaviour and sports celebrities -- Today's context -- Planning mega-event associations for the brand…”

Tabla de Contenidos: “…Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data - Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer - Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator…”

Tabla de Contenidos: “…-- 10.4.7 Controller tuning -- 10.4.8 Manual PI tuning -- 10.4.9 Ziegler-Nichols frequency response method -- 10.4.10 Autotuning -- 10.5 Instrumentation and Monitoring -- 10.5.1 Instruments -- 10.5.2 Estimating the oxygen uptake rate -- 10.6 Pumping -- 10.6.1 Pump characteristics -- 10.6.2 Relationship between flow rate and power -- 10.7 Essential Control Loops -- 10.7.1 Dissolved oxygen control -- 10.7.2 Ammonia-Based DO control -- 10.7.3 Recycle flow controls -- 10.7.4 Chemical precipitation -- 10.7.5 Anaerobic digestion process control -- 10.8 Minimising Operating Cost -- 10.8.1 Approaches -- 10.8.2 The human factor -- 10.9 Concluding Remarks -- Chapter 11: Evolutionary operation -- 11.1 Learning -- 11.2 Application to Sewage Treatment Plants -- 11.2.1 Plant improvement strategies -- 11.2.2 Learning curves -- 11.2.3 Startup phase -- 11.3 Long-Term Improvement -- 11.3.1 Potential -- 11.3.2 Tuning -- 11.3.3 Stress testing -- 11.3.4 Debottlenecking -- 11.3.5 Costs -- 11.4 EVOP Procedures -- 11.4.1 Method -- 11.4.2 Trend plots -- 11.4.3 General principles -- 11.5 Faux EVOP -- Appendix 1: A note on the statistics of variability -- Average and Median…”