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- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/08%3A__StringsThis page covers string manipulation in programming, including string operations, slicing, searching, formatting, and splitting/joining strings. It provides a chapter summary to enhance understanding ...This page covers string manipulation in programming, including string operations, slicing, searching, formatting, and splitting/joining strings. It provides a chapter summary to enhance understanding of these concepts. Additionally, it mentions user interface labels that aid in path settings and confirmation dialogues in a software application.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/12%3A_Recursion/12.05%3A_Using_Recursion_to_Solve_ProblemsThis page explores recursion in searching lists and solving the Three Towers problem. It details binary search, a recursive method for sorted lists that halves the search space by checking the middle ...This page explores recursion in searching lists and solving the Three Towers problem. It details binary search, a recursive method for sorted lists that halves the search space by checking the middle element. The Three Towers problem is similarly approached with recursion, organizing rings based on subproblems. Code examples highlight how recursion simplifies complex issues, enhancing efficiency in searching and problem-solving.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/07%3A_Deep_Learning_and_AI_Basics/7.03%3A_Introduction_to_Deep_LearningThis page provides an overview of deep learning principles, focusing on neural networks and the role of hidden layers in feature recognition and classification. It discusses loss functions, including ...This page provides an overview of deep learning principles, focusing on neural networks and the role of hidden layers in feature recognition and classification. It discusses loss functions, including mean squared error and binary cross entropy, crucial for training. The text highlights the use of sparse categorical cross entropy with softmax for multi-class tasks, demonstrating its application in classifying handwritten numerals using TensorFlow.
- https://eng.libretexts.org/Bookshelves/Mechanical_Engineering/Mechanics_of_Materials_(Roylance)/06%3A_Yield_and_Fracture/6.03%3A_Statistics_of_FractureThis page emphasizes the statistical analysis of fracture in high-strength materials, crucial for design related to human safety. It introduces key statistical measures, distribution functions (normal...This page emphasizes the statistical analysis of fracture in high-strength materials, crucial for design related to human safety. It introduces key statistical measures, distribution functions (normal and Weibull), and the importance of repeated measurements to reduce uncertainty in strength values. The t-distribution is highlighted for small samples, and connections between specimen volume and failure probability are discussed.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/04%3A_Inferential_Statistics_and_Regression_Analysis/4.04%3A_Analysis_of_Variance_(ANOVA)This page explains the one-way ANOVA process for comparing multiple means, covering hypothesis formulation, conditions, and calculations using the F-distribution. It highlights the null hypothesis of ...This page explains the one-way ANOVA process for comparing multiple means, covering hypothesis formulation, conditions, and calculations using the F-distribution. It highlights the null hypothesis of equal means versus an alternative of at least one differing mean. A practical example using Python's f_oneway function shows an F statistic of 4.388 and a p-value of 0.
