The Reinforcement Learning Workshop: Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems.
With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intellige...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , , , , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease. |
| Beschreibung: | Description based upon print version of record. Batch Normalization. |
| Beschreibung: | 1 online resource (821 p.) |
| ISBN: | 9781800209961 1800209967 |
Internformat
MARC
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| 049 | |a MAIN | ||
| 100 | 1 | |a Palmas, Alessandro. | |
| 245 | 1 | 4 | |a The Reinforcement Learning Workshop |h [electronic resource] : |b Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2020. | ||
| 300 | |a 1 online resource (821 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Reinforcement Learning -- Introduction -- Learning Paradigms -- Introduction to Learning Paradigms -- Supervised versus Unsupervised versus RL -- Classifying Common Problems into Learning Scenarios -- Predicting Whether an Image Contains a Dog or a Cat -- Detecting and Classifying All Dogs and Cats in an Image -- Playing Chess -- Fundamentals of Reinforcement Learning -- Elements of RL -- Agent -- Actions -- Environment -- Policy -- An Example of an Autonomous Driving Environment | |
| 505 | 8 | |a Exercise 1.01: Implementing a Toy Environment Using Python -- The Agent-Environment Interface -- What's the Agent? What's in the Environment? -- Environment Types -- Finite versus Continuous -- Deterministic versus Stochastic -- Fully Observable versus Partially Observable -- POMDP versus MDP -- Single Agents versus Multiple Agents -- An Action and Its Types -- Policy -- Stochastic Policies -- Policy Parameterizations -- Exercise 1.02: Implementing a Linear Policy -- Goals and Rewards -- Why Discount? -- Reinforcement Learning Frameworks -- OpenAI Gym -- Getting Started with Gym -- CartPole | |
| 505 | 8 | |a Gym Spaces -- Exercise 1.03: Creating a Space for Image Observations -- Rendering an Environment -- Rendering CartPole -- A Reinforcement Learning Loop with Gym -- Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym -- Activity 1.01: Measuring the Performance of a Random Agent -- OpenAI Baselines -- Getting Started with Baselines -- DQN on CartPole -- Applications of Reinforcement Learning -- Games -- Go -- Dota 2 -- StarCraft -- Robot Control -- Autonomous Driving -- Summary -- Chapter 2: Markov Decision Processes and Bellman Equations -- Introduction -- Markov Processes | |
| 505 | 8 | |a The Markov Property -- Markov Chains -- Markov Reward Processes -- Value Functions and Bellman Equations for MRPs -- Solving Linear Systems of an Equation Using SciPy -- Exercise 2.01: Finding the Value Function in an MRP -- Markov Decision Processes -- The State-Value Function and the Action-Value Function -- Bellman Optimality Equation -- Solving the Bellman Optimality Equation -- Solving MDPs -- Algorithm Categorization -- Value-Based Algorithms -- Policy Search Algorithms -- Linear Programming -- Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming -- Gridworld | |
| 505 | 8 | |a Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation | |
| 500 | |a Batch Normalization. | ||
| 520 | |a With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease. | ||
| 650 | 0 | |a Reinforcement learning. |0 http://id.loc.gov/authorities/subjects/sh92000704 | |
| 650 | 0 | |a Algorithms. |0 http://id.loc.gov/authorities/subjects/sh85003487 | |
| 650 | 2 | |a Algorithms |0 https://id.nlm.nih.gov/mesh/D000465 | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Programming Languages |x Python. |2 bisacsh | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Reinforcement learning |2 fast | |
| 700 | 1 | |a Ghelfi, Emanuele. | |
| 700 | 1 | |a Petre, Alexandra Galina. | |
| 700 | 1 | |a Kulkarni, Mayur. | |
| 700 | 1 | |a N.S., Anand. | |
| 700 | 1 | |a Nguyen, Quan. | |
| 700 | 1 | |a Sen, Aritra. | |
| 700 | 1 | |a So, Anthony |c (Data scientist) |1 https://id.oclc.org/worldcat/entity/E39PCjGVCDWxcCx8xrFc47wmr3 |0 http://id.loc.gov/authorities/names/no2021117553 | |
| 700 | 1 | |a Basak, Saikat. | |
| 758 | |i has work: |a The Reinforcement Learning Workshop (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG9J4QJbxqBWRt8TQKQ8YP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Palmas, Alessandro |t The Reinforcement Learning Workshop : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems |d Birmingham : Packt Publishing, Limited,c2020 |z 9781800200456 |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1223099995 |
|---|---|
| _version_ | 1813901692887891968 |
| adam_text | |
| any_adam_object | |
| author | Palmas, Alessandro |
| author2 | Ghelfi, Emanuele Petre, Alexandra Galina Kulkarni, Mayur N.S., Anand Nguyen, Quan Sen, Aritra So, Anthony (Data scientist) Basak, Saikat |
| author2_role | |
| author2_variant | e g eg a g p ag agp m k mk a n an q n qn a s as a s as s b sb |
| author_GND | http://id.loc.gov/authorities/names/no2021117553 |
| author_facet | Palmas, Alessandro Ghelfi, Emanuele Petre, Alexandra Galina Kulkarni, Mayur N.S., Anand Nguyen, Quan Sen, Aritra So, Anthony (Data scientist) Basak, Saikat |
| author_role | |
| author_sort | Palmas, Alessandro |
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| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.6 .P35 2020 |
| callnumber-search | Q325.6 .P35 2020 |
| callnumber-sort | Q 3325.6 P35 42020 |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBA |
| contents | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Reinforcement Learning -- Introduction -- Learning Paradigms -- Introduction to Learning Paradigms -- Supervised versus Unsupervised versus RL -- Classifying Common Problems into Learning Scenarios -- Predicting Whether an Image Contains a Dog or a Cat -- Detecting and Classifying All Dogs and Cats in an Image -- Playing Chess -- Fundamentals of Reinforcement Learning -- Elements of RL -- Agent -- Actions -- Environment -- Policy -- An Example of an Autonomous Driving Environment Exercise 1.01: Implementing a Toy Environment Using Python -- The Agent-Environment Interface -- What's the Agent? What's in the Environment? -- Environment Types -- Finite versus Continuous -- Deterministic versus Stochastic -- Fully Observable versus Partially Observable -- POMDP versus MDP -- Single Agents versus Multiple Agents -- An Action and Its Types -- Policy -- Stochastic Policies -- Policy Parameterizations -- Exercise 1.02: Implementing a Linear Policy -- Goals and Rewards -- Why Discount? -- Reinforcement Learning Frameworks -- OpenAI Gym -- Getting Started with Gym -- CartPole Gym Spaces -- Exercise 1.03: Creating a Space for Image Observations -- Rendering an Environment -- Rendering CartPole -- A Reinforcement Learning Loop with Gym -- Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym -- Activity 1.01: Measuring the Performance of a Random Agent -- OpenAI Baselines -- Getting Started with Baselines -- DQN on CartPole -- Applications of Reinforcement Learning -- Games -- Go -- Dota 2 -- StarCraft -- Robot Control -- Autonomous Driving -- Summary -- Chapter 2: Markov Decision Processes and Bellman Equations -- Introduction -- Markov Processes The Markov Property -- Markov Chains -- Markov Reward Processes -- Value Functions and Bellman Equations for MRPs -- Solving Linear Systems of an Equation Using SciPy -- Exercise 2.01: Finding the Value Function in an MRP -- Markov Decision Processes -- The State-Value Function and the Action-Value Function -- Bellman Optimality Equation -- Solving the Bellman Optimality Equation -- Solving MDPs -- Algorithm Categorization -- Value-Based Algorithms -- Policy Search Algorithms -- Linear Programming -- Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming -- Gridworld Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation |
| ctrlnum | (OCoLC)1223099995 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1223099995 |
| illustrated | Not Illustrated |
| indexdate | 2024-10-25T15:50:56Z |
| institution | BVB |
| isbn | 9781800209961 1800209967 |
| language | English |
| oclc_num | 1223099995 |
| open_access_boolean | |
| owner | MAIN |
| owner_facet | MAIN |
| physical | 1 online resource (821 p.) |
| psigel | ZDB-4-EBA |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Packt Publishing, Limited, |
| record_format | marc |
| spelling | Palmas, Alessandro. The Reinforcement Learning Workshop [electronic resource] : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. Birmingham : Packt Publishing, Limited, 2020. 1 online resource (821 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Description based upon print version of record. Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Reinforcement Learning -- Introduction -- Learning Paradigms -- Introduction to Learning Paradigms -- Supervised versus Unsupervised versus RL -- Classifying Common Problems into Learning Scenarios -- Predicting Whether an Image Contains a Dog or a Cat -- Detecting and Classifying All Dogs and Cats in an Image -- Playing Chess -- Fundamentals of Reinforcement Learning -- Elements of RL -- Agent -- Actions -- Environment -- Policy -- An Example of an Autonomous Driving Environment Exercise 1.01: Implementing a Toy Environment Using Python -- The Agent-Environment Interface -- What's the Agent? What's in the Environment? -- Environment Types -- Finite versus Continuous -- Deterministic versus Stochastic -- Fully Observable versus Partially Observable -- POMDP versus MDP -- Single Agents versus Multiple Agents -- An Action and Its Types -- Policy -- Stochastic Policies -- Policy Parameterizations -- Exercise 1.02: Implementing a Linear Policy -- Goals and Rewards -- Why Discount? -- Reinforcement Learning Frameworks -- OpenAI Gym -- Getting Started with Gym -- CartPole Gym Spaces -- Exercise 1.03: Creating a Space for Image Observations -- Rendering an Environment -- Rendering CartPole -- A Reinforcement Learning Loop with Gym -- Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym -- Activity 1.01: Measuring the Performance of a Random Agent -- OpenAI Baselines -- Getting Started with Baselines -- DQN on CartPole -- Applications of Reinforcement Learning -- Games -- Go -- Dota 2 -- StarCraft -- Robot Control -- Autonomous Driving -- Summary -- Chapter 2: Markov Decision Processes and Bellman Equations -- Introduction -- Markov Processes The Markov Property -- Markov Chains -- Markov Reward Processes -- Value Functions and Bellman Equations for MRPs -- Solving Linear Systems of an Equation Using SciPy -- Exercise 2.01: Finding the Value Function in an MRP -- Markov Decision Processes -- The State-Value Function and the Action-Value Function -- Bellman Optimality Equation -- Solving the Bellman Optimality Equation -- Solving MDPs -- Algorithm Categorization -- Value-Based Algorithms -- Policy Search Algorithms -- Linear Programming -- Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming -- Gridworld Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation Batch Normalization. With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease. Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Apprentissage par renforcement (Intelligence artificielle) Algorithmes. algorithms. aat Programming & scripting languages: general. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Programming Languages Python. bisacsh Algorithms fast Reinforcement learning fast Ghelfi, Emanuele. Petre, Alexandra Galina. Kulkarni, Mayur. N.S., Anand. Nguyen, Quan. Sen, Aritra. So, Anthony (Data scientist) https://id.oclc.org/worldcat/entity/E39PCjGVCDWxcCx8xrFc47wmr3 http://id.loc.gov/authorities/names/no2021117553 Basak, Saikat. has work: The Reinforcement Learning Workshop (Text) https://id.oclc.org/worldcat/entity/E39PCG9J4QJbxqBWRt8TQKQ8YP https://id.oclc.org/worldcat/ontology/hasWork Print version: Palmas, Alessandro The Reinforcement Learning Workshop : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems Birmingham : Packt Publishing, Limited,c2020 9781800200456 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2575333 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2575333 Volltext |
| spellingShingle | Palmas, Alessandro The Reinforcement Learning Workshop Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Reinforcement Learning -- Introduction -- Learning Paradigms -- Introduction to Learning Paradigms -- Supervised versus Unsupervised versus RL -- Classifying Common Problems into Learning Scenarios -- Predicting Whether an Image Contains a Dog or a Cat -- Detecting and Classifying All Dogs and Cats in an Image -- Playing Chess -- Fundamentals of Reinforcement Learning -- Elements of RL -- Agent -- Actions -- Environment -- Policy -- An Example of an Autonomous Driving Environment Exercise 1.01: Implementing a Toy Environment Using Python -- The Agent-Environment Interface -- What's the Agent? What's in the Environment? -- Environment Types -- Finite versus Continuous -- Deterministic versus Stochastic -- Fully Observable versus Partially Observable -- POMDP versus MDP -- Single Agents versus Multiple Agents -- An Action and Its Types -- Policy -- Stochastic Policies -- Policy Parameterizations -- Exercise 1.02: Implementing a Linear Policy -- Goals and Rewards -- Why Discount? -- Reinforcement Learning Frameworks -- OpenAI Gym -- Getting Started with Gym -- CartPole Gym Spaces -- Exercise 1.03: Creating a Space for Image Observations -- Rendering an Environment -- Rendering CartPole -- A Reinforcement Learning Loop with Gym -- Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym -- Activity 1.01: Measuring the Performance of a Random Agent -- OpenAI Baselines -- Getting Started with Baselines -- DQN on CartPole -- Applications of Reinforcement Learning -- Games -- Go -- Dota 2 -- StarCraft -- Robot Control -- Autonomous Driving -- Summary -- Chapter 2: Markov Decision Processes and Bellman Equations -- Introduction -- Markov Processes The Markov Property -- Markov Chains -- Markov Reward Processes -- Value Functions and Bellman Equations for MRPs -- Solving Linear Systems of an Equation Using SciPy -- Exercise 2.01: Finding the Value Function in an MRP -- Markov Decision Processes -- The State-Value Function and the Action-Value Function -- Bellman Optimality Equation -- Solving the Bellman Optimality Equation -- Solving MDPs -- Algorithm Categorization -- Value-Based Algorithms -- Policy Search Algorithms -- Linear Programming -- Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming -- Gridworld Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Apprentissage par renforcement (Intelligence artificielle) Algorithmes. algorithms. aat Programming & scripting languages: general. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Programming Languages Python. bisacsh Algorithms fast Reinforcement learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh92000704 http://id.loc.gov/authorities/subjects/sh85003487 https://id.nlm.nih.gov/mesh/D000465 |
| title | The Reinforcement Learning Workshop Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_auth | The Reinforcement Learning Workshop Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_exact_search | The Reinforcement Learning Workshop Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_full | The Reinforcement Learning Workshop [electronic resource] : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_fullStr | The Reinforcement Learning Workshop [electronic resource] : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_full_unstemmed | The Reinforcement Learning Workshop [electronic resource] : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| title_short | The Reinforcement Learning Workshop |
| title_sort | reinforcement learning workshop learn how to apply cutting edge reinforcement learning algorithms to a wide range of control problems |
| title_sub | Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. |
| topic | Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Apprentissage par renforcement (Intelligence artificielle) Algorithmes. algorithms. aat Programming & scripting languages: general. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Programming Languages Python. bisacsh Algorithms fast Reinforcement learning fast |
| topic_facet | Reinforcement learning. Algorithms. Algorithms Apprentissage par renforcement (Intelligence artificielle) Algorithmes. algorithms. Programming & scripting languages: general. Artificial intelligence. Neural networks & fuzzy systems. Computers Intelligence (AI) & Semantics. Computers Neural Networks. Computers Programming Languages Python. Reinforcement learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2575333 |
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