Tabla de Contenidos: “…Interactive Research as a Strategy for Practice-based Learninge -- 13.1 Introduction -- 13.2 Towards a Model of Competence Development -- 13.3 Cultural Context of Teachers’ Learning and Professional Growth -- 13.4 Interactive Research -- 13.5 The Interactive Processes – The ‘Quality Case’ -- 13.5.1 Local Schools’ Collective Competence Development -- 13.6 The Practice-based Model -- 13.6.1 Identifying Practice -- 13.6.2 Reflective Transformation -- 13.6.3 Joint Construction and Institutionalisation of Tools -- 13.6.4 Professional Growth and Remaking of Practice -- 14. …”

Tabla de Contenidos: “…-- Open questions and practical challenges -- Open questions -- Practical challenges for robotic reinforcement learning -- Key takeaways -- Summary -- Chapter 12: Deep Reinforcement Learning in Ad Tech -- Computational advertising challenges and bidding strategies -- Business models used in advertising -- Sponsored-search advertisements -- Search-advertisement management -- Adwords -- Bidding strategies of advertisers -- Real-time bidding by reinforcement learning in display advertising -- Summary -- Chapter 13: Reinforcement Learning in Image Processing -- Hierarchical object detection with deep reinforcement learning -- Related works -- Region-based convolution neural networks -- Spatial pyramid pooling networks -- Fast R-CNN -- Faster R-CNN -- You Look Only Once -- Single Shot Detector -- Hierarchical object detection model -- State -- Actions -- Reward -- Model and training -- Training specifics -- Summary -- Chapter 14: Deep Reinforcement Learning in NLP -- Text summarization -- Deep reinforced model for Abstractive Summarization -- Neural intra-attention model -- Intra-temporal attention on input sequence while decoding -- Intra-decoder attention -- Token generation and pointer -- Hybrid learning objective -- Supervised learning with teacher forcing -- Policy learning -- Mixed training objective function -- Text question answering -- Mixed objective and deep residual coattention for Question Answering -- Deep residual coattention encoder -- Mixed objective using self-critical policy learning -- Summary -- Appendix : Further topics in Reinforcement Learning…”

“…They are also excellent teachers, and provide didactically outstanding chapters. …”

“…Seine umfangreichen Erfahrungen bringt er in viele Präsentationen auf Konferenzen im gesamten deutschsprachigen Raum sowie in Artikeln und Büchern zum Thema Softwaretest ein.Stefan Gwihs ist begeisterter Softwareentwickler, Softwaretester und Testautomatisierer für die Nagarro GmbH, Wien. …”

Tabla de Contenidos: “…8.5.2 Wie Sie es nutzen sollten -- 8.5.3 Was es zu beachten gilt -- 8.6 Klickverhalten‌ und Besucherfluss‌ -- 8.6.1 Was gemessen werden kann -- 8.6.2 Wie Sie es nutzen sollten‌ -- 8.6.3 Was es zu beachten gilt -- 8.7 Beweggründe‌ und Aufgaben‌‌ von Besuchern -- 8.7.1 Was gemessen werden kann -- 8.7.2 Wie Sie es nutzen sollten -- 8.7.3 Was es zu beachten gilt -- 8.8 Verhalten und Ziele‌ von Nutzergruppen‌ und Personas‌ -- Kapitel 9: Inhaltsnutzung‌‌ -- 9.1 Genutzte Inhalte‌ -- 9.1.1 Was gemessen werden kann -- 9.1.2 Wie Sie es nutzen sollten -- 9.1.3 Was es zu beachten gilt -- 9.2 Inhaltsgruppen‌ -- 9.2.1 Was gemessen werden kann -- 9.2.2 Wie Sie es nutzen sollten -- 9.2.3 Was es zu beachten gilt -- 9.3 Ein‌‌- und Ausstiegsseiten‌‌ -- 9.3.1 Was gemessen werden kann -- 9.3.2 Wie Sie es nutzen sollten -- 9.3.3 Was es zu beachten gilt -- 9.4 Attraktivität‌‌ einer Seite -- 9.4.1 Was gemessen werden kann -- 9.4.2 Wie Sie es nutzen sollten -- 9.4.3 Was es zu beachten gilt -- 9.5 Geschwindigkeit,‌ Ausfälle‌ und Fehler‌‌ -- 9.5.1 Was gemessen werden kann -- 9.5.2 Wie Sie es nutzen sollten -- 9.5.3 Was es zu beachten gilt -- Kapitel 10: Spezifische Inhalte und digitale Kanäle -- 10.1 Mobile-Apps‌‌ -- 10.1.1 Was gemessen werden kann -- 10.1.2 Wie Sie es nutzen sollten -- 10.1.3 Was es zu beachten gilt -- 10.2 Social Media‌ und Social Networks‌ -- 10.2.1 Was gemessen werden kann -- 10.2.2 Wie Sie es nutzen sollten -- 10.2.3 Was es zu beachten gilt -- 10.3 Soziale Interaktionen‌‌ -- 10.3.1 Was gemessen werden kann -- 10.3.2 Wie Sie es nutzen sollten -- 10.3.3 Was es zu beachten gilt -- 10.4 Weblogs‌‌‌ -- 10.4.1 Was gemessen werden kann -- 10.4.2 Wie Sie es nutzen sollten -- 10.4.3 Was es zu beachten gilt -- 10.5 Dynamische Webanwendungen -- 10.5.1 Was gemessen werden kann -- 10.5.2 Wie Sie es nutzen sollten -- 10.5.3 Was es zu beachten gilt…”

Tabla de Contenidos: “…11.3.3 Prediction of Nifty score -- 11.3.3.1 Linear regression -- 11.3.3.2 Polynomial regression -- 11.3.3.3 Random forest -- 11.3.3.4 Gradient boost regressor -- 11.4 Results and experimental framework -- 11.4.1 Linear regression -- 11.4.2 Polynomial regression with degree 3 -- 11.4.3 Random forest regression -- 11.4.4 Gradient boost regressor -- 11.5 Conclusion -- References -- Further reading -- 12 Impact of COVID-19: a particular focus on Indian education system -- 12.1 Introduction -- 12.2 Impact of COVID-19 on education -- 12.2.1 Effect of home confinement on children and teachers -- 12.2.2 A multidimensional impact of uncertainty -- 12.3 Sustaining the education industry during COVID-19 -- 12.4 Conclusion -- References -- 13 Designing of Latent Dirichlet Allocation Based Prediction Model to Detect Midlife Crisis of Losing Jobs due to Prolonged... -- 13.1 Introduction -- 13.2 Literature survey -- 13.3 Methodology -- 13.3.1 Distinguishing midlife crisis symptoms -- 13.3.2 Designing of the prediction model -- 13.3.3 Application of LDA and statistical comparison -- 13.3.3.1 Formulation of Dirichlet distribution -- 13.3.3.2 Categorization in Bayesian model -- 13.3.3.3 Concept of topic modeling -- 13.4 Result and discussion -- 13.5 Conclusion and future scope -- References -- 14 Autonomous robotic system for ultraviolet disinfection -- 14.1 Introduction -- 14.2 Background -- 14.2.1 Ultraviolet light for disinfection -- 14.2.2 Exposure time for deactivation of the bacteria -- 14.2.3 Flow chart of UV bot control logic -- 14.2.4 Calculations related to the time for disinfection -- 14.3 Implementation -- 14.4 Model topology -- 14.4.1 UV-C light robotic vehicle -- 14.5 Conclusion -- References -- 15 Emerging health start-ups for economic feasibility: opportunities during COVID-19 -- 15.1 Introduction -- 15.2 Health-tech verticals for start-ups…”

Tabla de Contenidos: “…-- File types -- The upload limit -- Web host file management -- File management via the file system repository -- Summary -- Chapter 3: Courses, Users, and Roles -- A high-level overview -- Moodle courses -- Moodle users -- Moodle roles -- Summary -- Chapter 4: Course Management -- Course categories -- Course categories - an overview -- Managing course categories -- Adding course categories -- Course sub-categories -- Deleting course categories -- Organizing courses and categories -- Creating courses -- Course requests - enabling teachers to ask for new courses…”

Tabla de Contenidos: “…1 Einführung. 11 -- 1.1 Gegenstand der Studie . 11 -- 1.2 Ansätze der Leserforschung . 15 -- 1.3 Fragestellung . 21 -- 1.4 Methodisches Vorgehen . 27 -- 1.5 Aufbau der Studie . 31 -- 1.6 Begriffliche Klärungen . 34 -- 1.6.1 Lesebiographie . 34 -- 1.6.2 Lesesozialisation und literarische Sozialisation . 35 -- 1.6.3 Leseklima . 39 -- 1.6.4 Lesekultur und Buchkultur . 40 -- 2 Buch und Lesen im neunzehnten Jahrhundert . 45 -- 2.1 Historischer Überblick . 45 -- 2.2 Der Buchmarkt im neunzehnten Jahrhundert . 54 -- 2.2.1 Bedingungen des Buchmarktes . 54 -- 2.2.2 Verleger im neunzehnten Jahrhundert . 70 -- 2.2.3 Der schottische Buchmarkt im neunzehnten Jahrhundert . 74 -- 2.3 Leser im neunzehnten Jahrhundert . 80 -- 2.3.1 Leser und ihre Bildungsmöglichkeiten . 82 -- 2.3.2 Die viktorianische Autodidaktenkultur . 87 -- 2.4 Autobiographien als Quellen der historischen Leserforschung . 94 -- 2.4.1 Formen der autobiographischen Lebensbeschreibung . 96 -- 2.4.2 Theoretische Überlegungen zur Autobiographie . 97 -- 2.4.3 Geschichte der Autobiographie . 103 -- 2.4.4 Autobiographische Paratexte . 108 -- 2.4.5 Besonderheiten der Verlegerautobiographien als Quellen der historischen Leserforschung . 111 -- 3 Verleger als Leser . 117 -- 3.1 William (1800-1883) und Robert Chambers (1802-1872) als Leser . 117 -- 3.1.1 Die autobiographischen Quellen der Chambers-Brüder . 117 -- 3.1.1.1 Die Memoir of Robert Chambers with Autobiographic Reminiscences of William Chambers (1872) . 118 -- 3.1.1.2 Robert Chambers, Diary (1833) und autobiographisches Fragment (ca. 1860) . 120 -- 3.1.1.3 William Chambers, Story of a Long and Busy Life (1882) . 124 -- 3.1.1.4 Rezeption der Memoir of Robert Chambers . 125 -- 3.1.2 "A well-plenished table": Lesen in der schottischen Provinz . 128 -- 3.1.2.1 Biographischer Hintergrund der Chambers-Brüder . 128 -- 3.1.2.2 Selbst(er)findung durch Selbsthilfe . 136 -- 3.1.2.3 Der Schulbesuch der Chambers-Brüder . 141 -- 3.1.2.4 Mündlichkeit als Mittel (para-)literarischer Sozialisation . 155 -- 3.1.2.5 Zugang zu Büchern: Die Leihbibliothek Alexander Elders . 165 -- 3.1.2.6 Die Encyclopædia Britannica . 175 -- 3.1.3 William und Robert Chambers als Leser . 182 -- 3.2 Charles Kegan Paul (1828-1902) als Leser . 185 -- 3.2.1 Das autobiographische Quellenmaterial Pauls . 185 -- 3.2.1.1 Memories (1899) und Confessio viatoris (1891) . 186 -- 3.2.1.2 Rezeption der Memories . 193 -- 3.2.2 Lesesozialisation in der anglikanischen Mittelschicht . 195 -- 3.2.2.1 Biographischer Hintergrund C. …”

Tabla de Contenidos: “…Using an LSTM for time series prediction -- Data preparation -- Loading the dataset -- Slicing train and test by date -- Differencing a time series -- Scaling a time series -- Creating a lagged training set -- Input shape -- Data preparation glue -- Network output -- Network architecture -- Stateful versus stateless LSTMs -- Training -- Measuring performance -- Summary -- Chapter 10: Training LSTMs with Word Embeddings from Scratch -- An introduction to natural language processing -- Semantic analysis -- Document classification -- Vectorizing text -- NLP terminology -- Bag of Word models -- Stemming, lemmatization, and stopwords -- Count and TF-IDF vectorization -- Word embedding -- A quick example -- Learning word embeddings with prediction -- Learning word embeddings with counting -- Getting from words to documents -- Keras embedding layer -- 1D CNNs for natural language processing -- Case studies for document classifications -- Sentiment analysis with Keras embedding layers and LSTMs -- Preparing the data -- Input and embedding layer architecture -- LSTM layer -- Output layer -- Putting it all together -- Training the network -- Performance -- Document classification with and without GloVe -- Preparing the data -- Loading pretrained word vectors -- Input and embedding layer architecture -- Without GloVe vectors -- With GloVe vectors -- Convolution layers -- Output layer -- Putting it all together -- Training -- Performance -- Summary -- Chapter 11: Training Seq2Seq Models -- Sequence-to-sequence models -- Sequence-to-sequence model applications -- Sequence-to-sequence model architecture -- Encoders and decoders -- Characters versus words -- Teacher forcing -- Attention -- Translation metrics -- Machine translation -- Understanding the data -- Loading data -- One hot encoding -- Training network architecture -- Network architecture (for inference)…”

Tabla de Contenidos: “…Part 3: Attacks on Deployed AI -- Chapter 7: Evasion Attacks against Deployed AI -- Fundamentals of evasion attacks -- Importance of understanding evasion attacks -- Reconnaissance techniques for evasion attacks -- Perturbations and image evasion attack techniques -- Evasion attack scenarios -- One-step perturbation with FGSM -- Basic Iterative Method (BIM) -- Jacobian-based Saliency Map Attack (JSMA) -- Carlini and Wagner (C&amp -- W) attack -- Projected Gradient Descent (PGD) -- Adversarial patches - bridging digital and physical evasion techniques -- NLP evasion attacks with BERT using TextAttack -- Attack scenario - sentiment analysis -- Attack example -- Attack scenario - natural language inference -- Attack example -- Universal Adversarial Perturbations (UAPs) -- Attack scenario -- Attack example -- Black-box attacks with transferability -- Attack scenario -- Attack example -- Defending against evasion attacks -- Mitigation strategies overview -- Adversarial training -- Input preprocessing -- Model hardening techniques -- Model ensembles -- Certified defenses -- Summary -- Chapter 8: Privacy Attacks - Stealing Models -- Understanding privacy attacks -- Stealing models with model extraction attacks -- Functionally equivalent extraction -- Learning-based model extraction attacks -- Generative student-teacher extraction (distillation) attacks -- Attack example against our CIFAR-10 CNN -- Defenses and mitigations -- Prevention measures -- Detection measures -- Model ownership identification and recovery -- Summary -- Chapter 9: Privacy Attacks - Stealing Data -- Understanding model inversion attacks -- Types of model inversion attacks -- Exploitation of model confidence scores -- GAN-assisted model inversion -- Example model inversion attack -- Understanding inference attacks -- Attribute inference attacks -- Meta-classifiers…”

Tabla de Contenidos: “…Organizing courses into categories ; Providing an overview of course categories ; Managing course categories ; Creating courses ; Course creation workflows ; Creating a new course ; Custom course fields ; Course requests - enabling teachers to ask for new courses ; Managing courses in bulk ; Understanding course enrolment ; Internal enrolment ; Cohort enrolment and synchronization ; Database-driven enrolment ; Meta courses - sharing enrolment across courses ; Enrolment with payment ; Summary --…”

“…Die Verbreitung in Deutschland befördert mit Engagement, diversen Artikeln, Büchern und Konferenzbeiträgen. Sein aktuelles Steckenpferd ist The Responsibility ProcessTM. …”

Tabla de Contenidos: “…-- Conceptualize the Program or Code Goal -- Write Code -- Debug Code -- Debug Code That Won't Run -- Debug Code That Produces Specific Errors -- Debug Code That Produces Logical Errors -- Refactor Code -- Optimize Code -- Chapter 9 ChatGPT Prompts for HR and Training -- Draft Job Descriptions and Job Ads -- Draft Job Descriptions -- Draft Job Ads -- Develop Onboarding and Training Materials -- Create an Onboarding Guide for a New Employee -- Create a Checklist of Tasks for New Employees -- Create Training Scenarios -- Gather Feedback -- Understand What Kind of Feedback Mechanism ChatGPT Can Help You Implement -- Decide What You Want Feedback On -- Create Survey Questions -- Implement the Survey -- Analyze the Survey Responses -- Create a Feedback Report -- Chapter 10 ChatGPT Prompts for Sales and Marketing -- Create Marketing Content -- Write Blog Posts -- Create Other Social Media Content -- Draw Up Press Releases -- Write Marketing Emails -- Draw Up a Marketing Plan -- Generate Sales Content -- Write a Product Description -- Create a Sales Presentation -- Engage with Customers via Email -- Draft Individual Sales Email Messages -- Draft Batches of Personalized Email Messages -- Chapter 11 ChatGPT Prompts for Teachers -- Make Clear How Your Students Should Be Using AI -- Choose a Course Topic -- Write a Course Description -- Write Course Goals and Objectives -- Write Course Goals -- Write the Course Objectives -- Create Lesson Plans -- Create Reading Lists -- Get Ideas for Activities -- Develop Tests…”

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