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Squirrels don't like his rain. Cats don't like his rain. Worst of all, children don't like his rain, and this has the Heavy Little Cloud feeling pretty low. He quickly comes up with a plan to keep everyone happy. Unfortunately, his idea starts to cause some trouble along the way. Children around the world have fallen in love with the vibrant colors and fun illustrations in the "Clouds in the Wide Blue Sky" picture book series. Parents appreciate the positive underlying messages found within the pages. Join our fluffy little friend as he embarks on an adventure of self-acceptance and friendship.
This special e-edition combines all three print volumes of the collected enduring legends of Kerala in the Aithihyamaala, the garland of legends. Yakshis, gandharvas, gods and demi-gods. Famous poets and learned Ayurvedic doctors. Magicians, conceited kings and Kalari gurus. Faithful, intelligent elephants and their fatherly mahouts. A vibrant and diverse cast of characters brings to life the ancient stories. The original collection of 126 tales, were documented over 25 years and written in the 1900s by Kottaaraththil Sankunni. These stories of well-known figures in Kerala folklore were first published in Bhashaposhini, the renowned Malayalam literary magazine. This edition of 50 stories, meticulously curated and translated by Leela James, transports you to the magical world of history, myth and fantasy of more than 100 years ago. Wisdom and vice, revenge and loyalty, imagination and fact, faith and superstition are intricately intertwined to create a collector?s edition for lovers of legends, Malayalam folklore and Indian literature.
The dataset used in this book consists of daily weather observations from various locations in Australia spanning a 10-year period. The target variable is "RainTomorrow," which predicts whether it will rain the following day. The dataset comprises 23 attributes, including: DATE: The date of observation.; LOCATION: The name of the weather station's location.; MINTEMP: The minimum temperature in degrees Celsius.; MAXTEMP: The maximum temperature in degrees Celsius.; RAINFALL: The amount of rainfall recorded for the day in mm.; EVAPORATION: Class A pan evaporation in mm for the 24 hours until 9 am.; SUNSHINE: The number of hours of bright sunshine in a day.; WINDGUSTDIR: The direction of the strongest wind gust in the 24 hours until midnight.; WINDGUSTSPEED: The speed of the strongest wind gust in km/h in the 24 hours until midnight.; WINDDIR9AM: The direction of the wind at 9 am. The project utilizes several machine learning models, including K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling techniques, namely raw scaling, MinMax scaling, and standard scaling, are employed. These machine learning models are utilized to analyze the weather attributes and make predictions about the occurrence of rainfall. Each model has its strengths and may perform differently based on the characteristics of the dataset. Additionally, a GUI is developed using PyQt5 to visualize cross-validation scores, predicted values versus true values, confusion matrix, learning curves, decision boundaries, model performance, scalability, training loss, and training accuracy. These visualizations within the GUI provide a comprehensive understanding of the model's performance, learning behavior, decision-making boundaries, and the quality of its predictions. Users can leverage these insights to fine-tune the model and improve its accuracy and generalization capabilities. In addition, the GUI developed using PyQt5 also includes the capability to visualize features on a year-wise and month-wise basis. This functionality allows users to explore the variations and trends in different weather attributes across different years and months. With the year-wise and month-wise visualizations, users can gain insights into the temporal patterns and trends present in the weather data. It enables them to observe how specific attributes change over time and across different seasons, providing a deeper understanding of the weather patterns and their potential influence on rainfall occurrences.
Features step-by-step examples based on actual data and connects fundamental mathematical modeling skills and decision making concepts to everyday applicability Featuring key linear programming, matrix, and probability concepts, Finite Mathematics: Models and Applications emphasizes cross-disciplinary applications that relate mathematics to everyday life. The book provides a unique combination of practical mathematical applications to illustrate the wide use of mathematics in fields ranging from business, economics, finance, management, operations research, and the life and social sciences. In order to emphasize the main concepts of each chapter, Finite Mathematics: Models and Applications features plentiful pedagogical elements throughout such as special exercises, end notes, hints, select solutions, biographies of key mathematicians, boxed key principles, a glossary of important terms and topics, and an overview of use of technology. The book encourages the modeling of linear programs and their solutions and uses common computer software programs such as LINDO. In addition to extensive chapters on probability and statistics, principles and applications of matrices are included as well as topics for enrichment such as the Monte Carlo method, game theory, kinship matrices, and dynamic programming. Supplemented with online instructional support materials, the book features coverage including: Algebra Skills Mathematics of Finance Matrix Algebra Geometric Solutions Simplex Methods Application Models Set and Probability Relationships Random Variables and Probability Distributions Markov Chains Mathematical Statistics Enrichment in Finite Mathematics An ideal textbook, Finite Mathematics: Models and Applications is intended for students in fields from entrepreneurial and economic to environmental and social science, including many in the arts and humanities.
In The Open Future: Why Future Contingents are all False, Patrick Todd launches a sustained defense of a radical interpretation of the doctrine of the open future. He argues that all claims about undetermined aspects of the future are simply false. Todd argues that this theory is metaphysically more parsimonius than its rivals, and that objections to its logical and practical coherence are much overblown. Todd shows how proponents of this view can maintain classical logic, and argues that the view has substantial advantages over Ockhamist, supervaluationist, and relativist alternatives. Todd draws inspiration from theories of ''neg-raising'' in linguistics, from debates about omniscience within the philosophy of religion, and defends a crucial comparison between his account of future contingents and certain more familiar theories of counterfactuals. Further, Todd defends his theory of the open future from the charges that it cannot make sense of our practices of betting, makes our credences regarding future contingents unintelligible, and is at odds with proper norms of assertion. In the end, in Todd's classical open future, we have a compelling new solution to the longstanding problem of future contingents.