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The sounds of language : an introduction to phonetics and phonology by Zsiga, Elizabeth C.

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Managing nonprofit organizations in a policy world by Vaughan, Shannon K.

Contents: “…Resource Development: Grants -- The Role of Grants in the Nonprofit Sector -- Types of Grants -- Project or Program Grants -- Operating Grants -- Seed Money -- Challenge/Matching Grants -- Government Grants and Fee-for-Service Contracts -- Distribution of Government Grants -- Public Goods and Services via Government Contracts -- Foundation Funding -- Types of Foundations -- Preparing a Grant Proposal -- Getting Started -- Budget and Project Narratives -- Submitting the Proposal -- Public Policy Consequences of Grants and Contracts -- Government Funding of Policy Priorities -- Foundation Funding to Influence Policy -- Conclusion -- Questions for Review -- Assignment -- Suggested Readings -- Web Resources -- pt. …”
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The data science handbook by Cady, Field, 1984-

Contents: “…-- 16.2 The Great Divide: Language versus Statistics -- 16.3 Example: Sentiment Analysis on Stock Market Articles -- 16.4 Software and Datasets -- 16.5 Tokenization -- 16.6 Central Concept: Bag-of-Words -- 16.7 Word Weighting: TF-IDF -- 16.8 n-Grams -- 16.9 Stop Words -- 16.10 Lemmatization and Stemming 16.11 Synonyms -- 16.12 Part of Speech Tagging -- 16.13 Common Problems -- 16.14 Advanced NLP: Syntax Trees, Knowledge, and Understanding -- 16.15 Further Reading -- 16.16 Glossary -- Chapter 17 Time Series Analysis -- 17.1 Example: Predicting Wikipedia Page Views -- 17.2 A Typical Workflow -- 17.3 Time Series versus Time-Stamped Events -- 17.4 Resampling an Interpolation -- 17.5 Smoothing Signals -- 17.6 Logarithms and Other Transformations -- 17.7 Trends and Periodicity -- 17.8 Windowing -- 17.9 Brainstorming Simple Features -- 17.10 Better Features: Time Series as Vectors -- 17.11 Fourier Analysis: Sometimes a Magic Bullet -- 17.12 Time Series in Context: The Whole Suite of Features -- 17.13 Further Reading -- 17.14 Glossary -- Chapter 18 Probability -- 18.1 Flipping Coins: Bernoulli Random Variables -- 18.2 Throwing Darts: Uniform Random Variables -- 18.3 The Uniform Distribution and Pseudorandom Numbers -- 18.4 Nondiscrete, Noncontinuous Random Variables -- 18.5 Notation, Expectations, and Standard Deviation -- 18.6 Dependence, Marginal and Conditional Probability -- 18.7 Understanding the Tails -- 18.8 Binomial Distribution -- 18.9 Poisson Distribution -- 18.10 Normal Distribution -- 18.11 Multivariate Gaussian -- 18.12 Exponential Distribution -- 18.13 Log-Normal Distribution -- 18.14 Entropy 18.15 Further Reading -- 18.16 Glossary -- Chapter 19 Statistics -- 19.1 Statistics in Perspective -- 19.2 Bayesian versus Frequentist: Practical Tradeoffs and Differing Philosophies -- 19.3 Hypothesis Testing: Key Idea and Example -- 19.4 Multiple Hypothesis Testing -- 19.5 Parameter Estimation -- 19.6 Hypothesis Testing: t-Test -- 19.7 Confidence Intervals -- 19.8 Bayesian Statistics -- 19.9 Naive Bayesian Statistics -- 19.10 Bayesian Networks -- 19.11 Choosing Priors: Maximum Entropy or Domain Knowledge -- 19.12 Further Reading -- 19.13 Glossary -- Chapter 20 Programming Language Concepts -- 20.1 Programming Paradigms -- 20.2 Compilation and Interpretation -- 20.3 Type Systems -- 20.4 Further Reading -- 20.5 Glossary -- Chapter 21 Performance and Computer Memory -- 21.1 Example Script -- 21.2 Algorithm Performance and Big-O Notation -- 21.3 Some Classic Problems: Sorting a List and Binary Search -- 21.4 Amortized Performance and Average Performance -- 21.5 Two Principles: Reducing Overhead and Managing Memory -- 21.6 Performance Tip: Use Numerical Libraries When Applicable -- 21.7 Performance Tip: Delete Large Structures You Don't Need -- 21.8 Performance Tip: Use Built-In Functions When Possible -- 21.9 Performance Tip: Avoid Superfluous Function Calls -- 21.10 Performance Tip: Avoid Creating Large New Objects -- 21.11 Further Reading -- 21.12 Glossary -- Part III Specialized or Advanced Topics -- Chapter 22 Computer Memory and Data Structures -- 22.1 Virtual Memory, the Stack, and the Heap -- 22.2 Example C Program -- 22.3 Data Types and Arrays in Memory -- 22.4 Structs -- 22.5 Pointers, the Stack, and the Heap -- 22.6 Key Data Structures -- 22.7 Further Reading -- 22.8 Glossary -- Chapter 23 Maximum Likelihood Estimation and Optimization -- 23.1 Maximum Likelihood Estimation -- 23.2 A Simple Example: Fitting a Line -- 23.3 Another Example: Logistic Regression -- 23.4 Optimization -- 23.5 Gradient Descent and Convex Optimization -- 23.6 Convex Optimization -- 23.7 Stochastic Gradient Descent -- 23.8 Further Reading -- 23.9 Glossary -- Chapter 24 Advanced Classifiers -- 24.1 A Note on Libraries -- 24.2 Basic Deep Learning -- 24.3 Convolutional Neural Networks -- 24.4 Different Types of Layers.…”
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