03551nam 2200517Ia 450 991096679830332120251116153215.097801997025340199702535(MiAaPQ)EBC7037384(CKB)24235073900041(MiAaPQ)EBC415891(Au-PeEL)EBL415891(CaPaEBR)ebr10160649(CaONFJC)MIL84541(OCoLC)99806476(OCoLC)437096310(Au-PeEL)EBL7037384(OCoLC)1336401573(EXLCZ)992423507390004120060412d2007 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierThe great American crime decline /Franklin E. Zimring1st ed.New York Oxford University Press2007xiv, 258 pStudies in crime and public policyIncludes bibliographical references and index.Intro -- Contents -- Part I: What Happened in the 1990s? -- 1 The Size and Character of the Crime Decline -- 2 The Environment for Optimism: Crime Trends and Attitudes about the Effectiveness of Crime Policies -- Part II: The Search for Causes -- 3 The Usual Suspects: Imprisonment, Demography, and the Economy -- 4 Progeny of the 1990s: Three New Explanations of Decline -- Part III: Two New Perspectives -- 5 Which Twin Has the Toni? Some Statistical Lessons from Canada -- 6 New York City's Natural Experiment -- Part IV: Twenty-First Century Lessons -- 7 What Happens Next? -- 8 Seven Lessons from the 1990s -- Appendix 1 Crime and Abortion Policy in Europe, Canada, and Australia -- Appendix 2 Supplementary Statistics on Crime Trends in Canada during the 1990s -- Appendix 3 Trends for the City of New York and the United States during the 1990s -- Appendix 4 Measuring the Extent of Decline in Selected High-Decline Cities -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y -- Z.Part I What Happened in the 1990s? 1. The Size and Character of the Crime Decline 2. The Environment for Optimism: Crime Trends and Attitudes about the Effectiveness of Crime Policies Part II The Search for Causes 3. The Usual Suspects: Imprisonment, Demography, and the Economy 4. Progeny of the 1990s: Three New Explanations of Decline Part III Two New Perspectives 5. Which Twin Has the Toni? Some Statistical Lessons from Canada 6. New York City's Natural Experiment Part IV Twenty-First Century Lessons 7. What Happens Next? 8. Seven Lessons from the 1990s Appendix 1: Crime and Abortion Policy in Europe, Canada, and Australia Appendix 2: Supplementary Statistics on Crime Trends in Canada during the 1990s Appendix 3: Trends for the City of New York and the United States during the 1990s Appendix 4: Measuring the Extent of Decline in Selected High-Decline Cities References Index.Studies in crime and public policy.CrimeUnited StatesHistory20th centuryUnited StatesEconomic conditions1981-2001United StatesSocial conditions1980-CrimeHistory364.97309/049Zimring Franklin E472956MiAaPQMiAaPQMiAaPQ9910966798303321The great American crime decline4464115UNINA05144nam 22005293 450 991104665370332120250502195427.097988886512619798888651278(MiAaPQ)EBC31679727(Au-PeEL)EBL31679727(CKB)35559670600041(Exl-AI)31679727(OCoLC)1456760717(MiAaPQ)EBC31929459(Au-PeEL)EBL31929459(OCoLC)1505733150(EXLCZ)993555967060004120250502d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine learning in Elixir learning to learn with Nx and Axon /Sean MoriarityFirst edition.[Raleigh, North Carolina] :The Pragmatic Programmers, LLC,[2024]©20241 online resource (359 pages)9798888650349 Includes bibliographical references.Cover -- Table of Contents -- Disclaimer -- Acknowledgments -- Preface -- Why Elixir for Machine Learning? -- Who This Book Is For -- What's in This Book -- How to Use This Book -- Part I-Foundations of Machine Learning -- 1. Make Machines That Learn -- Classifying Flowers -- Learning with Elixir -- Wrapping Up -- 2. Get Comfortable with Nx -- Thinking in Tensors -- Using Nx Operations -- Representing the World -- Going from def to defn -- Wrapping Up -- 3. Harness the Power of Math -- Understanding Machine Learning Math -- Speaking the Language of Data -- Thinking Probabilistically -- Tracking Change -- Wrapping Up -- 4. Optimize Everything -- Learning with Optimization -- Regularizing to Generalize -- Descending Gradients -- Peering into the Black Box -- Wrapping Up -- 5. Traditional Machine Learning -- Learning Linearly -- Learning from Your Surroundings -- Using Clustering -- Making Decisions -- Wrapping Up -- Part II-Deep Learning -- 6. Go Deep with Axon -- Understanding the Need for Deep Learning -- Breaking Down a Neural Network -- Creating Neural Networks with Axon -- Wrapping Up -- 7. Learn to See -- Identifying Cats and Dogs -- Introducing Convolutional Neural Networks -- Improving the Training Process -- Going Beyond Image Classification -- Wrapping Up -- 8. Stop Reinventing the Wheel -- Identifying Cats and Dogs Again -- Fine-Tuning Your Model -- Understanding Transfer Learning -- Taking Advantage of the Machine Learning Ecosystem -- Wrapping Up -- 9. Understand Text -- Classifying Movie Reviews -- Introducing Recurrent Neural Networks -- Understanding Recurrent Neural Networks -- Wrapping Up -- 10. Forecast the Future -- Predicting Stock Prices -- Using CNNs for Single-Step Prediction -- Using RNNs for Time-Series Prediction -- Tempering Expectations -- Wrapping Up -- 11. Model Everything with Transformers -- Paying Attention.Going from RNNs to Transformers -- Using Transformers with Bumblebee -- Wrapping Up -- 12. Learn Without Supervision -- Compressing Data with Autoencoders -- Learning a Structured Latent -- Generating with GANs -- Learning Without Supervision in Practice -- Wrapping Up -- Part III-Machine Learning in Practice -- 13. Put Machine Learning into Practice -- Deciding to Use Machine Learning -- Setting Up the Application -- Integrating Nx with Phoenix -- Seeding Your Databases -- Building the Search LiveView -- Wrapping Up -- 14. That's a Wrap -- Learning from Experience -- Diffusing Innovation -- Talking to Large Language Models -- Compressing Knowledge -- Moving Forward -- Bibliography -- Index -- - DIGITS - -- - A - -- - B - -- - C - -- - D - -- - E - -- - F - -- - G - -- - H - -- - I - -- - J - -- - K - -- - L - -- - M - -- - N - -- - O - -- - P - -- - Q - -- - R - -- - S - -- - T - -- - U - -- - V - -- - W - -- - X - -- - Y - -- - Z -.Machine Learning in Elixir, authored by Sean Moriarity, explores the integration of machine learning capabilities within the Elixir programming language using the Nx ecosystem. The book provides a comprehensive guide for Elixir programmers to develop machine learning models and applications, covering foundational concepts, deep learning techniques, and practical implementation strategies. It aims to equip developers with the skills needed to use Elixir for machine learning tasks, traditionally dominated by languages like Python. The book also highlights the advantages of functional programming in machine learning and offers practical examples and tools to facilitate learning. It is intended for software developers and those interested in exploring machine learning through the lens of Elixir.Generated by AI.Machine learningElixir (Computer program language)Machine learning.Elixir (Computer program language)006.31Moriarity Sean1829258MiAaPQMiAaPQMiAaPQBOOK9911046653703321Machine learning in Elixir4468521UNINA