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[null,"1.","Record Nr.",null,"UNINA9910151959503321"]
[null,null,"Autore",null,"Chen Zhiyuan (Computer scientist)"]
[null,null,"Titolo",null,"Lifelong machine learning / / Zhiyuan Chen, Bing Liu"]
[null,null,"Pubbl/distr/stampa",null,"[San Rafael, California] : , : Morgan & Claypool, , 2017"]
[null,null,"ISBN",null,"1-62705-877-X"]
[null,null,"Descrizione fisica",null,"1 online resource (147 pages) : illustrations (some color)"]
[null,null,"Collana",null,"Synthesis lectures on artificial intelligence and machine learning, , 1939-4616 ; ; # 33"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Part of: Synthesis digital library of engineering and computer science."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 111-125)."]
[null,null,"Nota di contenuto",null,"1. Introduction -- 1.1 A brief history of lifelong learning -- 1.2 Definition of lifelong learning -- 1.3 Lifelong learning system architecture -- 1.4 Evaluation methodology -- 1.5 Role of big data in lifelong learning -- 1.6 Outline of the book --"]
[{"$nulls":4},"2. Related learning paradigms -- 2.1 Transfer learning -- 2.1.1 Structural correspondence learning -- 2.1.2 Naïve Bayes transfer classifier -- 2.1.3 Deep learning in transfer learning -- 2.1.4 Difference from lifelong learning -- 2.2 Multi-task learning -- 2.2.1 Task relatedness in multi-task learning -- 2.2.2 GO-MTL: multi-task learning using latent basis -- 2.2.3 Deep learning in multi-task learning -- 2.2.4 Difference from lifelong learning -- 2.3 Online learning -- 2.3.1 Difference from lifelong learning -- 2.4 Reinforcement learning -- 2.4.1 Difference from lifelong learning -- 2.5 Summary --"]
[{"$nulls":4},"3. Lifelong supervised learning -- 3.1 Definition and overview -- 3.2 Lifelong memory-based learning -- 3.2.1 Two memory-based learning methods -- 3.2.2 Learning a new representation for lifelong learning -- 3.3 Lifelong neural networks -- 3.3.1 MTL Net -- 3.3.2 Lifelong EBNN -- 3.4 Cumulative learning and self-motivated learning -- 3.4.1 Training a cumulative learning model -- 3.4.2 Testing a cumulative learning model -- 3.4.3 Open world learning for unseen class detection -- 3.5 ELLA: an efficient lifelong learning algorithm -- 3.5.1 Problem setting -- 3.5.2 Objective function -- 3.5.3 Dealing with the first inefficiency -- 3.5.4 Dealing with the second inefficiency -- 3.5.5 Active task selection -- 3.6 LSC: lifelong sentiment classification -- 3.6.1 Naïve Bayesian text classification -- 3.6.2 Basic ideas of LSC -- 3.6.3 LSC technique -- 3.7 Summary and evaluation datasets --"]
[{"$nulls":4},"4. Lifelong unsupervised learning -- 4.1 Lifelong topic modeling -- 4.2 LTM: a lifelong topic model -- 4.2.1 LTM model -- 4.2.2 Topic knowledge mining -- 4.2.3 Incorporating past knowledge -- 4.2.4 Conditional distribution of Gibbs sampler -- 4.3 AMC: a lifelong topic model for small data -- 4.3.1 Overall algorithm of AMC -- 4.3.2 Mining must-link knowledge -- 4.3.3 Mining cannot-link knowledge -- 4.3.4 Extended Pólya Urn model -- 4.3.5 Sampling distributions in Gibbs sampler -- 4.4 Lifelong information extraction -- 4.4.1 Lifelong learning through recommendation -- 4.4.2 AER algorithm -- 4.4.3 Knowledge learning -- 4.4.4 Recommendation using past knowledge -- 4.5 Lifelong-RL: lifelong relaxation labeling -- 4.5.1 Relaxation labeling -- 4.5.2 Lifelong relaxation labeling -- 4.6 Summary and evaluation datasets --"]
[{"$nulls":4},"5. Lifelong semi-supervised learning for information extraction -- 5.1 NELL: a never ending language learner -- 5.2 NELL architecture -- 5.3 Extractors and learning in NELL -- 5.4 Coupling constraints in NELL -- 5.5 Summary --"]
[{"$nulls":4},"6. Lifelong reinforcement learning -- 6.1 Lifelong reinforcement learning through multiple environments -- 6.1.1 Acquiring and incorporating bias -- 6.2 Hierarchical Bayesian lifelong reinforcement learning -- 6.2.1 Motivation -- 6.2.2 Hierarchical Bayesian approach -- 6.2.3 MTRL algorithm -- 6.2.4 Updating hierarchical model parameters -- 6.2.5 Sampling an MDP -- 6.3 PG-ELLA: lifelong policy gradient reinforcement learning -- 6.3.1 Policy gradient reinforcement learning -- 6.3.2 Policy gradient lifelong learning setting -- 6.3.3 Objective function and optimization -- 6.3.4 Safe policy search for lifelong learning -- 6.3.5 Cross-domain lifelong reinforcement learning -- 6.4 Summary and evaluation datasets --"]
[{"$nulls":4},"7. Conclusion and future directions -- Bibliography -- Authors' biographies."]
[null,null,null,"Sommario/riassunto","Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning."]
[null,"2.","Record Nr.",null,"UNINA9911008982603321"]
[null,null,"Autore",null,"Serdar Burak"]
[null,null,"Titolo",null,"Effective Concurrency in Go : Develop, Analyze, and Troubleshoot High Performance Concurrent Applications with Ease"]
[null,null,"Pubbl/distr/stampa",null,"Birmingham : , : Packt Publishing, Limited, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9781804615980"]
[{"$nulls":4},"1804615986"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (212 pages)"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"Go (Computer program language)"]
[{"$nulls":4},"Computer multitasking"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Chapter 1: Concurrency - A  High-Level Overview -- Technical Requirements -- Concurrency and parallelism -- Shared memory versus message passing -- Atomicity, race, deadlocks, and starvation -- Summary -- Question -- Further reading -- Chapter 2: Go Concurrency Primitives -- Technical Requirements -- Goroutines -- Channels -- Mutex -- Wait groups -- Condition variables -- Summary -- Questions -- Chapter 3: The Go Memory Model -- Why a memory model is necessary -- The happened-before relationship between memory operations -- Synchronization characteristics of Go concurrency primitives -- Package initialization -- Goroutines -- Channels -- Mutexes -- Atomic memory operations -- Map, Once, and WaitGroup -- Summary -- Further reading -- Chapter 4: Some Well-Known  Concurrency Problems -- Technical Requirements -- The producer-consumer problem -- The dining philosophers problem -- Rate limiting -- Summary -- Chapter 5: Worker Pools and Pipelines -- Technical Requirements -- Worker pools -- Pipelines, fan-out, and fan-in -- Asynchronous pipeline -- Fan-out/fan-in -- Fan-in with ordering -- Summary -- Questions -- Chapter 6: Error Handling -- Error handling -- Pipelines -- Servers -- Panics -- Summary -- Chapter 7: Timers and Tickers -- Technical Requirements -- Timers - running something later -- Tickers - running something periodically -- Heartbeats -- Summary "]
[{"$nulls":4},"-- Chapter 8: Handling Requests Concurrently -- Technical Requirements -- The context, cancelations, and timeouts -- Backend services -- Distributing work and collecting results -- Semaphores - limiting concurrency -- Streaming data -- Dealing with multiple streams -- Summary -- Chapter 9: Atomic Memory Operations -- Technical Requirements -- Memory guarantees -- Compare and swap."]
[{"$nulls":4},"Practical uses of atomics -- Counters -- Heartbeat and progress meter -- Cancellations -- Detecting change -- Summary -- Chapter 10: Troubleshooting  Concurrency Issues -- Technical Requirements -- Reading stack traces -- Detecting failures and healing -- Debugging anomalies -- Summary -- Further reading -- Index -- Other Books You May Enjoy."]
[null,null,null,"Sommario/riassunto","Gain a deep understanding of concurrency and learn how to leverage concurrent algorithms to build high-throughput data processing applications, network servers and clients that scale. Key Features Learn about the Go concurrency primitives, Go memory model, and common concurrency patterns Develop the insights on how to model solutions to real-life problems using concurrency Explore practical techniques to analyze how concurrent programs behave Book Description The Go language has been gaining momentum due to its treatment of concurrency as a core language feature, making concurrent programming more accessible than ever. However, concurrency is still an inherently difficult skill to master, since it requires the development of the right mindset to decompose problems into concurrent components correctly. This book will guide you in deepening your understanding of concurrency and show you how to make the most of its advantages. You'll start by learning what guarantees are offered by the language when running concurrent programs. Through multiple examples, you will see how to use this information to develop concurrent algorithms that run without data races and complete successfully. You'll also find out all you need to know about multiple common concurrency patterns, such as worker pools, asynchronous pipelines, fan-in/fan-out, scheduling periodic or future tasks, and error and panic handling in goroutines. The central theme of this book is to give you, the developer, an understanding of why concurrent programs behave the way they do, and how they can be used to build correct programs that work the same way in all platforms. By the time you finish the final chapter, you'll be able to develop, analyze, and troubleshoot concurrent algorithms written in Go. What you will learn Understand basic concurrency concepts and problems Learn about Go concurrency primitives and how they work Learn about the Go memory model and why it is important Understand how to use common concurrency patterns See how you can deal with errors in a concurrent program Discover useful techniques for troubleshooting Who this book is for If you are a developer with basic knowledge of Go and are looking to gain expertise in highly concurrent backend application development, then this book is for you. Intermediate Go developers who want to make their backend systems more robust and scalable will also find plenty of useful information. Prior exposure Go is a prerequisite."]