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What is Transfer Learning

Chapter 12
Philosophy of Artificial Intelligence and Its Place in Society
A high-performance learning algorithm that is trained by data already collected or used by other machine learning structures. This method is very effective when the source of the data necessary is too scarce or expensive to gather.
Published in Chapter:
Mind Uploading in Artificial Intelligence
Jason Wissinger (Waynesburg University, USA) and Elizabeth Baoying Wang (Waynesburg University, USA)
Copyright: © 2023 |Pages: 12
DOI: 10.4018/978-1-6684-9591-9.ch012
Abstract
Mind uploading is the futurist idea of emulating all brain processes of an individual on a computer. Progress towards achieving this technology is currently limited by society's capability to study the human brain and the development of complex artificial neural networks capable of emulating the brain's architecture. The goal of this chapter is to provide a brief history of both categories, discuss the progress made, and note the roadblocks hindering future research. Then, by examining the roadblocks of neuroscience and artificial intelligence together, this chapter will outline a way to overcome their respective limitations by using the other field's strengths.
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Chapter 1
Generative AI in Higher Education
The practice of applying knowledge or models developed for one task to a different but related task. This approach is particularly useful for accelerating or improving the performance of AI models.
Published in Chapter: Generative AI in Higher Education; From: The Role of Generative AI in the Communication Classroom
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Chapter 89
Machine Learning Approach to Art Authentication
The process of leveraging existing learning models to facilitate the initial learning of related new problems.
Published in Chapter: Machine Learning Approach to Art Authentication; From: Encyclopedia of Data Science and Machine Learning
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Chapter 6
EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild
A method in deep learning in which a model which is a well trained on a large dataset, can be reused for the similar task with fewer data. With this method, it reduces the training time for the model as there is no need to train the model from scratch.
Published in Chapter: EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild; From: Design and Implementation of Healthcare Biometric Systems
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Chapter 3
Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays
Transfer learning refers to a set of techniques that can store the knowledge acquired from learning one problem (dataset) to another problem. For instance, a model that can recognize different types of housecats may be useful to train a model to recognize different types of lions. Transfer learning can also be useful in the case where outdated data needs to be updated (Pan, 2009 AU36: The in-text citation "Pan, 2009" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter: Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 5
Deep Learning for Sentiment Analysis: An Overview and Perspectives
A collective term for machine learning techniques concerned with adapting a model across different domains and/or tasks.
Published in Chapter: Deep Learning for Sentiment Analysis: An Overview and Perspectives; From: Natural Language Processing for Global and Local Business
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Chapter 79
Automatic Moderation of User-Generated Content
A machine learning method where a model developed for a task is reused as the starting point for a model on a second task.
Published in Chapter: Automatic Moderation of User-Generated Content; From: Encyclopedia of Data Science and Machine Learning
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Chapter 5
Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education
A machine learning system that takes existing, previously learned data and applies it to new tasks and activities.
Published in Chapter: Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education; From: Exploring the Ethical Implications of Generative AI
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Chapter 142
Multi-Label Classification
Using the model created from one task, to help with a different task.
Published in Chapter: Multi-Label Classification; From: Encyclopedia of Business Analytics and Optimization
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Chapter 13
Plant Disease Classification Using Deep Learning Techniques
It refers to the process of leveraging knowledge from one task to another related task, allowing a model to learn more efficiently and effectively with less training data. It involves using a pre-trained model as a starting point and adapting it to a new problem domain.
Published in Chapter: Plant Disease Classification Using Deep Learning Techniques; From: Effective AI, Blockchain, and E-Governance Applications for Knowledge Discovery and Management
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Chapter 9
Lightweight ConvNet Model for American Sign Language Hand Gesture Recognition
Transfer learning is the concept of breaking free from the isolated learning paradigm and applying what you've learned to solve related problems.
Published in Chapter: Lightweight ConvNet Model for American Sign Language Hand Gesture Recognition; From: Challenges and Applications for Hand Gesture Recognition
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Chapter 11
Transfer Learning
methods in machine learning that improve learning in a target task by transferring knowledge from one or more related source tasks.
Published in Chapter: Transfer Learning; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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